1,387 results on '"Adjacency matrix"'
Search Results
2. The Optimal Graph Whose Least Eigenvalue is Minimal among All Graphs via 1-2 Adjacency Matrix
- Author
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Gul, Lubna, primary, Ali, Gohar, additional, Waheed, Usama, additional, and Aamir, Nudrat, additional
- Published
- 2021
- Full Text
- View/download PDF
3. Cascading Failure Model for Command and Control Networks Based on an m-Order Adjacency Matrix
- Author
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Bo Chen, Xiaoshuang Chen, Xiu-e Gao, and Yunming Wang
- Subjects
Article Subject ,Degree (graph theory) ,Computer Networks and Communications ,Computer science ,Node (networking) ,Survivability ,TK5101-6720 ,Topology ,01 natural sciences ,Cascading failure ,010305 fluids & plasmas ,Computer Science Applications ,Nonlinear system ,0103 physical sciences ,Command and control ,Telecommunication ,Adjacency matrix ,Isolation (database systems) ,010306 general physics - Abstract
Cascading failure models for command and control networks (C2 networks) continue to be a challenging and important research area. Current solutions share a common limitation because the solutions focus only on the importance of each node in isolation using one index rather than considering the contribution degree of neighboring nodes, which makes the initial load definition inaccurate and affects the cascading invulnerability of the network. To address this limitation, a new cascading failure model for C2 networks is proposed. The new model CFMAdjM, which is based on an m-order adjacency matrix, proposes a method of initial load definition using the contribution degree of m-order neighboring nodes and defines the nonlinear load capacity model according to the nonlinear relationship between load and capacity. Finally, the influence of model parameters on the cascading failure of C2 networks is analyzed through simulation, and the results demonstrate that the new model effectively resists the cascading failure and enhances the survivability of the network by defining the initial load and adjusting the coefficient appropriately.
- Published
- 2018
4. The Adjacency Matrix of One Type of Directed Graph and the Jacobsthal Numbers and Their Determinantal Representation
- Author
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Fatih Yilmaz, Durmuş Bozkurt, and Selçuk Üniversitesi
- Subjects
Discrete mathematics ,Jacobsthal number ,Degree matrix ,Article Subject ,lcsh:Mathematics ,Applied Mathematics ,lcsh:QA1-939 ,Combinatorics ,Integer matrix ,Graph energy ,Seidel adjacency matrix ,Adjacency list ,Regular graph ,Adjacency matrix ,Mathematics - Abstract
WOS: 000307579300001, Recently there is huge interest in graph theory and intensive study on computing integer powers of matrices. In this paper, we consider one type of directed graph. Then we obtain a general form of the adjacency matrices of the graph. By using the well-known property which states the (i, j) entry of A(m) (A is adjacency matrix) is equal to the number of walks of length m from vertex i to vertex j, we show that elements of mth positive integer power of the adjacency matrix correspond to well-known Jacobsthal numbers. As a consequence, we give a Cassini-like formula for Jacobsthal numbers. We also give a matrix whose permanents are Jacobsthal numbers., Selcuk UniversitySelcuk University, This research is supported by Selcuk University Research Project Coordinatorship (BAP).
- Published
- 2012
5. Cascading Failure Model for Command and Control Networks Based on an m-Order Adjacency Matrix
- Author
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Wang, Yun-ming, primary, Chen, Bo, additional, Chen, Xiao-shuang, additional, and Gao, Xiu-e, additional
- Published
- 2018
- Full Text
- View/download PDF
6. Incorporating Adaptive Sparse Graph Convolutional Neural Networks for Segmentation of Organs at Risk in Radiotherapy.
- Author
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Hu, Junjie, Yu, Chengrong, Zhu, Shengqian, and Zhang, Haixian
- Abstract
Precisely segmenting the organs at risk (OARs) in computed tomography (CT) plays an important role in radiotherapy's treatment planning, aiding in the protection of critical tissues during irradiation. Renowned deep convolutional neural networks (DCNNs) and prevailing transformer-based architectures are widely utilized to accomplish the segmentation task, showcasing advantages in capturing local and contextual characteristics. Graph convolutional networks (GCNs) are another specialized model designed for processing the nongrid dataset, e.g., citation relationship. The DCNNs and GCNs are considered as two distinct models applicable to the grid and nongrid datasets, respectively. Motivated by the recently developed dynamic-channel GCN (DCGCN) that attempts to leverage the graph structure to enhance the feature extracted by the DCNNs, this paper proposes a novel architecture termed adaptive sparse GCN (ASGCN) to mitigate the inherent limitations in DCGCN from the aspect of node's representation and adjacency matrix's construction. For the node's representation, the global average pooling used in the DCGCN is replaced by the learning mechanism to accommodate the segmentation task. For the adjacency matrix, an adaptive regularization strategy is leveraged to penalize the coefficient in the adjacency matrix, resulting in a sparse one that can better exploit the relationships between nodes. Rigorous experiments on multiple OARs' segmentation tasks of the head and neck demonstrate that the proposed ASGCN can effectively improve the segmentation accuracy. Comparison between the proposed method and other prevalent architectures further confirms the superiority of the ASGCN. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
7. The Adjacency Matrix of One Type of Directed Graph and the Jacobsthal Numbers and Their Determinantal Representation.
- Author
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Yılmaz, Fatih and Bozkurt, Durmuş
- Subjects
- *
MATRICES (Mathematics) , *DIRECTED graphs , *INTEGERS , *NUMBER theory , *MATHEMATICAL forms , *MATHEMATICAL formulas , *MATHEMATICAL analysis - Abstract
Recently there is huge interest in graph theory and intensive study on computing integer powers of matrices. In this paper, we consider one type of directed graph. Then we obtain a general form of the adjacency matrices of the graph. By using the well-known property which states the (i, j) entry of Am (A is adjacency matrix) is equal to the number of walks of length m from vertex i to vertex j, we show that elements of mth positive integer power of the adjacency matrix correspond to well-known Jacobsthal numbers. As a consequence, we give a Cassini-like formula for Jacobsthal numbers. We also give a matrix whose permanents are Jacobsthal numbers. [ABSTRACT FROM AUTHOR]
- Published
- 2012
- Full Text
- View/download PDF
8. Optimal Representation of Large-Scale Graph Data Based on Grid Clustering and K2-Tree.
- Author
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Li, Fengying, Yang, Enyi, Ma, Anqiao, and Dong, Rongsheng
- Subjects
DATA compression ,REPRESENTATIONS of graphs ,ALGORITHMS ,TECHNOLOGY - Abstract
The application of appropriate graph data compression technology to store and manipulate graph data with tens of thousands of nodes and edges is a prerequisite for analyzing large-scale graph data. The traditional K
2 -tree representation scheme mechanically partitions the adjacency matrix, which causes the dense interval to be split, resulting in additional storage overhead. As the size of the graph data increases, the query time of K2 -tree continues to increase. In view of the above problems, we propose a compact representation scheme for graph data based on grid clustering and K2 -tree. Firstly, we divide the adjacency matrix into several grids of the same size. Then, we continuously filter and merge these grids until grid density satisfies the given density threshold. Finally, for each large grid that meets the density, K2 -tree compact representation is performed. On this basis, we further give the relevant node neighbor query algorithm. The experimental results show that compared with the current best K2 -BDC algorithm, our scheme can achieve better time/space tradeoff. [ABSTRACT FROM AUTHOR]- Published
- 2020
- Full Text
- View/download PDF
9. Cascading Failure Model for Command and Control Networks Based on an m-Order Adjacency Matrix.
- Author
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Yun-ming Wang, Bo Chen, Xiao-shuang Chen, and Xiu-e Gao
- Subjects
INFORMATION services ,SCIENTIFIC community ,SCIENTIFIC errors ,SCIENCE & society ,SCIENTIFIC method - Abstract
Cascading failure models for command and control networks (C2 networks) continue to be a challenging and important research area. Current solutions share a common limitation because the solutions focus only on the importance of each node in isolation using one index rather than considering the contribution degree of neighboring nodes, which makes the initial load definition inaccurate and affects the cascading invulnerability of the network. To address this limitation, a new cascading failure model for C2 networks is proposed. +e new model CFMAdjM, which is based on an m-order adjacency matrix, proposes a method of initial load definition using the contribution degree of m-order neighboring nodes and defines the nonlinear load capacity model according to the nonlinear relationship between load and capacity. Finally, the influence of model parameters on the cascading failure of C2 networks is analyzed through simulation, and the results demonstrate that the new model effectively resists the cascading failure and enhances the survivability of the network by defining the initial load and adjusting the coefficient appropriately. [ABSTRACT FROM AUTHOR]
- Published
- 2018
- Full Text
- View/download PDF
10. DEMLP: DeepWalk Embedding in MLP for miRNA-Disease Association Prediction
- Author
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Yu Zhuang, Xinzeng Wang, Xun Wang, Fuyu Wang, and Sibo Qiao
- Subjects
Article Subject ,business.industry ,Computer science ,Association (object-oriented programming) ,Node (networking) ,Construct (python library) ,Machine learning ,computer.software_genre ,Control and Systems Engineering ,Interaction network ,Multilayer perceptron ,T1-995 ,Embedding ,Adjacency matrix ,Artificial intelligence ,Electrical and Electronic Engineering ,business ,Representation (mathematics) ,Instrumentation ,computer ,Technology (General) - Abstract
miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA-disease associations, a lot of high-efficiency computational methods by computer have been developed, based on a network generated by miRNA-disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node from the network. Secondly, how to measure whether there is an association between two nodes of the network is also an important problem. In our study, we innovatively integrate graph node embedding with a multilayer perceptron and propose a method DEMLP. To begin with, we construct a miRNA-disease network by miRNA-disease adjacency matrix (MDA). Then, low-dimensional embedding representation vectors of nodes are learned from the miRNA-disease network by DeepWalk. Finally, we use these low-dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA–disease association information can effectively predict miRNA-disease associations. To evaluate the effectiveness of DEMLP in a miRNA-disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC-AUC computed result value of DEMLP is 0.943, and the PR-AUC value of DEMLP is 0.937. Compared with other state-of-the-art methods, our method shows good performance using only the miRNA-disease interaction network.
- Published
- 2021
11. Sign-Consensus of Linear Multiagent Systems under a State Observer Protocol
- Author
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Wei-Lu Diao and Cui-Qin Ma
- Subjects
0209 industrial biotechnology ,Multidisciplinary ,Article Subject ,General Computer Science ,Computer science ,020208 electrical & electronic engineering ,02 engineering and technology ,State (functional analysis) ,Topology ,lcsh:QA75.5-76.95 ,Computer Science::Multiagent Systems ,020901 industrial engineering & automation ,Computer Science::Systems and Control ,Ordinary differential equation ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:Electronic computers. Computer science ,Adjacency matrix ,State observer ,Matrix analysis ,Sign (mathematics) - Abstract
The sign-consensus problem for linear time-invariant systems under signed digraph is considered. The information of the agents’ states is reconstructed, and then, a state observer-type sign-consensus protocol is proposed, whose performance is analyzed using matrix analysis and ordinary differential equation theory. Sufficient conditions for ensuring sign-consensus are given. It is proven that if the adjacency matrix of the signed digraph has strong Perron–Frobenius property or is eventually positive, sign-consensus can be achieved under the proposed protocol. In particular, conventional consensus is a special case of sign-consensus under mild conditions.
- Published
- 2019
12. Road Travel Time Prediction Based on Improved Graph Convolutional Network
- Author
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Miao Xu and Hongfei Liu
- Subjects
Article Subject ,Computer Networks and Communications ,Computer science ,Covariance matrix ,TK5101-6720 ,computer.software_genre ,Computer Science Applications ,System dynamics ,Traffic congestion ,Robustness (computer science) ,Information system ,Telecommunication ,Graph (abstract data type) ,Adjacency matrix ,Data mining ,Baseline (configuration management) ,computer - Abstract
Travel time prediction is playing an increasingly important part in advanced traveler information system (ATIS), which is of great significance to alleviate urban traffic congestion. Although graph convolutional networks have been widely used in road network traffic prediction, spatiotemporal dynamic modeling of urban traffic is still an intractable task. In this study, we propose an improved graph convolutional network (IGC-Net) for travel time prediction. Specifically, we design a modified adjacency matrix by fusing distance and correlation matrix with original adjacency matrix to capture spatial dynamic feature. We then establish three components based on temporal property to capture recent, daily-periodic, and weekly periodic correlations. The comparison experiments with baseline models and variants on a real-world dataset in Beijing are conducted. The results show that the IGC-Net outperforms baseline models in different prediction horizons and has stronger robustness for dynamic traffic prediction.
- Published
- 2021
13. Feature Extraction of Sequence of Keystrokes in Fixed Text Using the Multivariate Hawkes Process
- Author
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Yuchen Zhang, Fulin Li, and Chang Zhang
- Subjects
021110 strategic, defence & security studies ,Sequence ,Feature data ,Article Subject ,Computer science ,business.industry ,General Mathematics ,Feature extraction ,0211 other engineering and technologies ,General Engineering ,Process (computing) ,Pattern recognition ,02 engineering and technology ,Keystroke logging ,Engineering (General). Civil engineering (General) ,Kernel (statistics) ,Classifier (linguistics) ,0202 electrical engineering, electronic engineering, information engineering ,QA1-939 ,020201 artificial intelligence & image processing ,Adjacency matrix ,Artificial intelligence ,TA1-2040 ,business ,Mathematics - Abstract
In this paper, we propose a new method of extracting the features of keystrokes. The Hawkes process based on exponential excitation kernel was used to model the sequence of keystrokes in fixed text, and the intensity function vector and adjacency matrix of the model obtained through training were regarded as the characteristics of the keystrokes. A visual analysis was carried out on the CMU keystroke raw data and the feature data extracted using the proposed method. We used one-class classifier to compare the classification effect of CMU keystroke raw data and the feature data extracted by the Hawkes process model and POHMM model. The experimental results show that the feature data extracted using the proposed method contains rich information to distinguish users. In addition, the feature data extracted using the proposed method has a slightly better classification performance than the original CMU keystroke data for some users who are not easy to distinguish.
- Published
- 2021
14. On Spectral Radius and Energy of a Graph with Self-Loops.
- Author
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Vivek Anchan, Deekshitha, H. J., Gowtham, and D'Souza, Sabitha
- Subjects
NONNEGATIVE matrices ,ABSOLUTE value ,EIGENVALUES ,BINDING energy - Abstract
The spectral radius of a square matrix is the maximum among absolute values of its eigenvalues. Suppose a square matrix is nonnegative; then, by Perron–Frobenius theory, it will be one among its eigenvalues. In this paper, Perron–Frobenius theory for adjacency matrix of graph with self-loops A G S will be explored. Specifically, it discusses the nontrivial existence of Perron–Frobenius eigenvalue and eigenvector pair in the matrix A G S − σ n I , where σ denotes the number of self-loops. Also, Koolen–Moulton type bound for the energy of graph G S is explored. In addition, the existence of a graph with self-loops for every odd energy is proved. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF
15. Identifying Fake Accounts on Social Networks Based on Graph Analysis and Classification Algorithms
- Author
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Amir Masoud Rahmani, Mohammad Ebrahim Shiri, and Mohammadreza Mohammadrezaei
- Subjects
Power graph analysis ,Jaccard index ,Information retrieval ,Article Subject ,Social network ,Computer Networks and Communications ,Computer science ,business.industry ,020206 networking & telecommunications ,02 engineering and technology ,Support vector machine ,Statistical classification ,lcsh:Technology (General) ,0202 electrical engineering, electronic engineering, information engineering ,lcsh:T1-995 ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,False positive rate ,Adjacency matrix ,lcsh:Science (General) ,business ,lcsh:Q1-390 ,Information Systems - Abstract
Social networks have become popular due to the ability to connect people around the world and share videos, photos, and communications. One of the security challenges in these networks, which have become a major concern for users, is creating fake accounts. In this paper, a new model which is based on similarity between the users’ friends’ networks was proposed in order to discover fake accounts in social networks. Similarity measures such as common friends, cosine, Jaccard, L1-measure, and weight similarity were calculated from the adjacency matrix of the corresponding graph of the social network. To evaluate the proposed model, all steps were implemented on the Twitter dataset. It was found that the Medium Gaussian SVM algorithm predicts fake accounts with high area under the curve=1 and low false positive rate=0.02.
- Published
- 2018
16. On Detecting and Removing Superficial Redundancy in Vector Databases
- Author
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Noemí DeCastro-García, Mario Fernández Rodríguez, Miguel V. Carriegos, Ángel Luis Muñoz Castañeda, Algebra, and Escuela de Ingenierias Industrial e Informatica
- Subjects
Article Subject ,Biblioteconomía ,1203.12 Bancos de Datos ,Computer science ,General Mathematics ,02 engineering and technology ,computer.software_genre ,0202 electrical engineering, electronic engineering, information engineering ,Redundancy (engineering) ,MapReduce ,Adjacency matrix ,Optimización de datos ,Computer Science::Databases ,computer.programming_language ,Database ,lcsh:Mathematics ,General Engineering ,020207 software engineering ,Directed graph ,Python (programming language) ,lcsh:QA1-939 ,Ciberseguridad ,lcsh:TA1-2040 ,020201 artificial intelligence & image processing ,lcsh:Engineering (General). Civil engineering (General) ,computer ,Python - Abstract
A mathematical model is proposed in order to obtain an automatized tool to remove any unnecessary data, to compute the level of the redundancy, and to recover the original and filtered database, at any time of the process, in a vector database. This type of database can be modeled as an oriented directed graph. Thus, the database is characterized by an adjacency matrix. Therefore, a record is no longer a row but a matrix. Then, the problem of cleaning redundancies is addressed from a theoretical point of view. Superficial redundancy is measured and filtered by using the 1-norm of a matrix. Algorithms are presented by Python and MapReduce, and a case study of a real cybersecurity database is performed.
- Published
- 2018
17. Behavior Pattern Mining from Traffic and Its Application to Network Anomaly Detection.
- Author
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Meng, Yongwei, Qin, Tao, Li, Shancang, and Wang, Pinghui
- Subjects
ANOMALY detection (Computer security) ,MATRIX decomposition ,INTERNET protocol address ,MINES & mineral resources - Abstract
Accurately detecting and identifying abnormal behaviors on the Internet are a challenging task. In this work, an anomaly detection scheme is proposed that employs the behavior attribute matrix and adjacency matrix to characterize user behavior patterns. Then, anomaly detection is conducted by analyzing the residual matrix. By analyzing network traffic and anomaly characteristics, we construct the behavior attribute matrix, which incorporates seven features that characterize user behavior patterns. To include the effects of network environment, we employ the similarity between IP addresses to form the adjacency matrix. Further, we employ CUR matrix decomposition to mine the changing trends of the matrices and obtain the residual pattern characteristics that are used to detect anomalies. To validate the effectiveness and accuracy of the proposed scheme, two datasets are used: (1) the public MAWI dataset, collected from the WIDE backbone network, which is used to validate accuracy; (2) the campus network dataset, collected from the northwest center of Chinese Education and Research Network (CERNET), which is used to verify practicability. The experimental results demonstrate that the proposed scheme can not only accurately detect and identify abnormal behaviors but also trace the source of anomalies. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
18. AGCN-T: A Traffic Flow Prediction Model for Spatial-Temporal Network Dynamics.
- Author
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Feng, Jian, Yu, Lang, and Ma, Rui
- Subjects
TRAFFIC flow ,TRAFFIC patterns ,PREDICTION models ,INTELLIGENT transportation systems ,DEMAND forecasting ,TRAFFIC engineering ,TIME-varying networks - Abstract
Traffic prediction is the key for Intelligent Transport Systems (ITS) to achieve traffic control and traffic guidance, and the key challenge is that traffic flow has complex spatial-temporal dependence and nonlinear dynamics. Aiming at the lack of the ability to model complex and dynamic spatial-temporal dependencies in current research, this paper proposes a traffic flow prediction model Attention based Graph Convolution Network (GCN) and Transformer (AGCN-T) to model spatial-temporal network dynamics of traffic flow, which can extract dynamic spatial dependence and long-distance temporal dependence to improve the accuracy of multistep traffic prediction. AGCN-T consists of three modules. In the spatial dependency extraction module, according to the similarity of historical traffic flow sequences of different loop detectors, an adjacency matrix for the road network is constructed based on a sequence similarity calculation method, Predictive Power Score (PPS), to express latent spatial dependency; and then GCN is used on the adjacency matrix to capture the global spatial correlation and Transformer is used to capture dynamic spatial dependency from the most recently flow sequences. And then, the dynamic spatial dependency is merged with the global spatial correlation to obtain the overall spatial dependency pattern. In the temporal dependency extraction module, the temporal dependency pattern of each traffic flow sequence is learned by the temporal Transformer. The prediction module integrates both patterns to form spatial-temporal dependency patterns and performs multistep traffic flow prediction. Four sets of experiments are performed on three actual traffic datasets to show that AGCN-T can effectively capture the dynamic spatial-temporal dependency of the traffic network, and its prediction performance and efficiency are better than existing baselines. AGCN-T can effectively capture the dynamics in traffic flow. In addition to traffic flow prediction, it can also be applied to other spatial-temporal prediction tasks, such as passenger demand prediction and crowd flow prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
19. Parallel Nonnegative Matrix Factorization with Manifold Regularization
- Author
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Yihang Chen, Zheng Shan, and Fudong Liu
- Subjects
Computer engineering. Computer hardware ,Speedup ,Article Subject ,General Computer Science ,Computer science ,0211 other engineering and technologies ,02 engineering and technology ,Non-negative matrix factorization ,law.invention ,TK7885-7895 ,ComputingMethodologies_PATTERNRECOGNITION ,law ,Signal Processing ,Scalability ,0202 electrical engineering, electronic engineering, information engineering ,Graph (abstract data type) ,020201 artificial intelligence & image processing ,Adjacency matrix ,Nonnegative matrix ,Electrical and Electronic Engineering ,Representation (mathematics) ,Algorithm ,Manifold (fluid mechanics) ,021101 geological & geomatics engineering - Abstract
Nonnegative matrix factorization (NMF) decomposes a high-dimensional nonnegative matrix into the product of two reduced dimensional nonnegative matrices. However, conventional NMF neither qualifies large-scale datasets as it maintains all data in memory nor preserves the geometrical structure of data which is needed in some practical tasks. In this paper, we propose a parallel NMF with manifold regularization method (PNMF-M) to overcome the aforementioned deficiencies by parallelizing the manifold regularized NMF on distributed computing system. In particular, PNMF-M distributes both data samples and factor matrices to multiple computing nodes instead of loading the whole dataset in a single node and updates both factor matrices locally on each node. In this way, PNMF-M succeeds to resolve the pressure of memory consumption for large-scale datasets and to speed up the computation by parallelization. For constructing the adjacency matrix in manifold regularization, we propose a two-step distributed graph construction method, which is proved to be equivalent to the batch construction method. Experimental results on popular text corpora and image datasets demonstrate that PNMF-M significantly improves both scalability and time efficiency of conventional NMF thanks to the parallelization on distributed computing system; meanwhile it significantly enhances the representation ability of conventional NMF thanks to the incorporated manifold regularization.
- Published
- 2018
20. Road Travel Time Prediction Based on Improved Graph Convolutional Network.
- Author
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Xu, Miao and Liu, Hongfei
- Subjects
CITY traffic ,TRAFFIC congestion ,FORECASTING ,INFORMATION storage & retrieval systems ,DYNAMIC models ,PREDICTION models - Abstract
Travel time prediction is playing an increasingly important part in advanced traveler information system (ATIS), which is of great significance to alleviate urban traffic congestion. Although graph convolutional networks have been widely used in road network traffic prediction, spatiotemporal dynamic modeling of urban traffic is still an intractable task. In this study, we propose an improved graph convolutional network (IGC-Net) for travel time prediction. Specifically, we design a modified adjacency matrix by fusing distance and correlation matrix with original adjacency matrix to capture spatial dynamic feature. We then establish three components based on temporal property to capture recent, daily-periodic, and weekly periodic correlations. The comparison experiments with baseline models and variants on a real-world dataset in Beijing are conducted. The results show that the IGC-Net outperforms baseline models in different prediction horizons and has stronger robustness for dynamic traffic prediction. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
21. Some New Results on Various Graph Energies of the Splitting Graph
- Author
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Imran Ahmed, Zheng-Qing Chu, Tariq Javed Zia, Saima Nazeer, and Sana Shahid
- Subjects
Article Subject ,010405 organic chemistry ,Chemistry ,010401 analytical chemistry ,General Chemistry ,Absolute value (algebra) ,01 natural sciences ,0104 chemical sciences ,Combinatorics ,lcsh:Chemistry ,lcsh:QD1-999 ,Simple (abstract algebra) ,Graph (abstract data type) ,Adjacency matrix ,Energy (signal processing) ,Connectivity ,Eigenvalues and eigenvectors - Abstract
The energy of a simple connected graphGis equal to the sum of the absolute value of eigenvalues of the graphGwhere the eigenvalue of a graphGis the eigenvalue of its adjacency matrixAG. Ultimately, scores of various graph energies have been originated. It has been shown in this paper that the different graph energies of the regular splitting graphS′Gis a multiple of corresponding energy of a given graphG.
- Published
- 2019
22. Application and Analysis of Hypergraph Association Rule Redundancy Algorithm in Data Mining.
- Author
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Pang, Huanli and Zhou, Lianzhe
- Subjects
DATA mining ,ASSOCIATION rule mining ,BIG data ,DIRECTED graphs ,GLOBAL optimization ,ALGORITHMS ,MATHEMATICAL optimization - Abstract
In order to realize "from individual data research to data system research" and "from passive data verification to active discovery," this study proposes a hypergraph-based association rule redundancy processing algorithm in data mining. This study introduces the concepts of hypergraph and system, explores the establishment of hypergraph on a three-dimensional matrix model, and adopts a new hyperedge definition method according to the characteristics of big data and the concept of the system, which improves the ability to deal with problems; the association rules are transformed into a directed hypergraph, and the adjacency matrix is redefined. The detection of redundancy and loops is transformed into the processing of connected blocks and cycles in the hypergraph. The experimental results show that two UCI datasets were selected, namely, the balloons dataset and the shuttle landing control dataset, in which the minimum support and minimum confidence of the balloons dataset are both 5%. The dataset has 4 attributes, and 18 association rules are obtained through the Aprior algorithm. Although the running time of the coevolution algorithm is slightly longer than that of the other two global optimization algorithms, the running time is completely within the acceptable range. Moreover, due to the effective introduction of the idea of coevolution, compared with the use of the other two algorithms for association rule mining, it not only has a better mining quality but also has a significant advantage in the ability to jump out of the local optimal solution, realizing the search of high-quality association rules in high-dimensional datasets. Conclusion. This model provides a new idea and method for the redundant processing of association rules. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
23. The Research on Risk Distribution and Evolution of Deep Foundation Pit Construction Adjacent to Existing Tunnels Based on Complex Network.
- Author
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Jiang, Jie, Liu, Guangyang, Huang, Xi, and Ou, Xiaoduo
- Subjects
BORED piles ,SUBWAY tunnels ,TUNNELS ,CONSTRUCTION management ,TIME series analysis ,ECOLOGICAL risk assessment ,STRIP mining - Abstract
The construction of deep foundation pits adjacent to existing subway tunnels faces enormous challenges, and once a safety accident occurs, there are often mass injuries that cause substantial economic losses. However, there are many shortcomings and defects in the traditional methods of assessing the safety of the pit itself and the existing tunnel in the construction of the deep foundation pit adjacent to existing tunnels. This study establishes an optimized complex network-based dynamic risk assessment model to dynamically assess the overall risk of deep foundation pits in adjacent existing tunnels systematically, solving the challenges of inaccurate risk assessment and inaccurate description of correlations between nonstationary time series data. In this study, we first divide the monitoring data into time windows and describe the correlation between nonstationary time-series monitoring data within each window based on the MF-DCCA method and the threshold method, and establish the adjacency matrix to prepare for the establishment of an optimized complex network model. Secondly, based on the adjacency matrix, a complex network model under different time windows is constructed, and risk assessment indexes are established through the topological parameters of the complex network model to explore the evolution of risk in time and space, so as to realize the risk distribution and quantitative evolution assessment of the system which is deep foundation pit adjacent to existing subway tunnels. Finally, the proposed method is tested by taking the Nanning underground comprehensive utilization project as an example. The results show that this method can quantify the risk of the construction of deep foundation pits adjacent to existing tunnels more effectively than the traditional method to describe the evolution law better. It has important guiding significance for strengthening the safety risk monitoring and safety management of the construction system of deep foundation pits adjacent to existing tunnels. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
24. A Similarity Search Using Molecular Topological Graphs
- Author
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Haruki Nakamura and Yoshifumi Fukunishi
- Subjects
Models, Molecular ,Article Subject ,lcsh:Biotechnology ,Health, Toxicology and Mutagenesis ,Nearest neighbor search ,Drug Evaluation, Preclinical ,lcsh:Medicine ,Similarity measure ,Topology ,Partial charge ,Matrix (mathematics) ,Path length ,lcsh:TP248.13-248.65 ,Genetics ,Molecule ,Computer Simulation ,Adjacency matrix ,Molecular Biology ,Mathematics ,lcsh:R ,General Medicine ,Models, Chemical ,Molecular Medicine ,Algorithms ,Research Article ,Biotechnology - Abstract
A molecular similarity measure has been developed using molecular topological graphs and atomic partial charges. Two kinds of topological graphs were used. One is the ordinary adjacency matrix and the other is a matrix which represents the minimum path length between two atoms of the molecule. The ordinary adjacency matrix is suitable to compare the local structures of molecules such as functional groups, and the other matrix is suitable to compare the global structures of molecules. The combination of these two matrices gave a similarity measure. This method was applied toin silicodrug screening, and the results showed that it was effective as a similarity measure.
- Published
- 2009
25. Research on the Spectral Domain Graph Convolution Collaborative Filtering Algorithm Based on Reinforcement Learning and Chebyshev.
- Author
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Wang, Song, Zeng, Cheng, Song, Bailing, Huang, Xuxiang, and Zhou, Sicheng
- Subjects
GRAPH algorithms ,REINFORCEMENT learning ,SINGULAR value decomposition ,ALGORITHMS ,FUNCTIONAL analysis - Abstract
To solve the abovementioned problem, we propose a collaborative filtering recommendation algorithm that incorporates singular value decomposition (SVD) and Chebyshev truncation in spectral domain convolution. Firstly, the SVD algorithm is used to optimize the adjacency matrix, mine the potential association information between users and items, and expand the user-item adjacency matrix. Finally, based on the MovieLens-1M public dataset, the proposed algorithm (CBSVD-SCF) is compared with other commonly used algorithms. The results show that the article optimizes the recommendation effect of the algorithm based on the traditional collaborative filtering algorithm by combining the temporal order and sequence of user interaction information, as well as the popularity of items and the activity of users; the experimental results on MovieLens show that the optimized collaborative filtering recommendation algorithm can effectively improve the recommendation effect. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
26. Critical Nodes Identification of Scientific Achievement Commercialization Network under k-Core.
- Author
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Weng, Wuyan, Li, Zi, Qiu, Qirong, and Cheng, Junheng
- Subjects
SIMILARITY (Physics) ,COMMERCIALIZATION ,TIME series analysis ,ACHIEVEMENT ,MULTICASTING (Computer networks) - Abstract
Aiming to improve the commercialization efficiency of scientific innovative achievements, this paper utilizes the time series visualization method to construct the time series network of each subsystem. After that, the network similarity is calculated by the cosine similarity theorem. On this basis, a new multilayer network adjacency matrix is obtained. With the adoption of k-core technology, the critical nodes can be identified to study the transformation efficiency of the innovation value in the network. Finally, according to the provincial innovation value transformation data of China from 1998 to 2016, an empirical study was carried out to calculate and analyze the transformation efficiency of innovation achievements in 30 provinces. The results indicate that (1) the transformation efficiency of innovation value can be expressed by the structure of the time series network constructed by the input-output vectors; (2) the mapping relationship of the value transformation vectors could be reflected by the cosine similarity of the time series network, while the transformation efficiency of innovation value could be identified using the k-core; and (3) the transformation efficiency of innovation value in three coastal provinces is relatively higher, while that of the rest of the provinces is roughly the same among the 30 provinces. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
27. An Algorithm for Identifying the Isomorphism of Planar Multiple Joint and Gear Train Kinematic Chains
- Author
-
Peng He and Yanhuo Zou
- Subjects
Kinematic chain ,0209 industrial biotechnology ,Sequence ,Article Subject ,General Mathematics ,Kinematic diagram ,lcsh:Mathematics ,General Engineering ,02 engineering and technology ,Kinematics ,lcsh:QA1-939 ,Gear train ,020303 mechanical engineering & transports ,020901 industrial engineering & automation ,0203 mechanical engineering ,lcsh:TA1-2040 ,Node (circuits) ,Isomorphism ,Adjacency matrix ,lcsh:Engineering (General). Civil engineering (General) ,Algorithm ,Mathematics - Abstract
Isomorphism identification of kinematic chains is one of the most important and challenging mathematical problems in the field of mechanism structure synthesis. In this paper, a new algorithm to identify the isomorphism of planar multiple joint and gear train kinematic chains has been presented. Firstly, the topological model (TM) and the corresponding weighted adjacency matrix (WAM) are introduced to describe the two types of kinematic chains, respectively. Then, the equivalent circuit model (ECM) of TM is established and solved by using circuit analysis method. The solved node voltage sequence (NVS) is used to determine the correspondence of vertices in two isomorphism identification kinematic chains, so an algorithm to identify two specific types of isomorphic kinematic chains has been obtained. Lastly, some typical examples are carried out to prove that it is an accurate, efficient, and easy mathematical algorithm to be realized by computer.
- Published
- 2016
28. Some Properties on Estrada Index of Folded Hypercubes Networks
- Author
-
Jia-Bao Liu, Xiang-Feng Pan, and Jinde Cao
- Subjects
Discrete mathematics ,Article Subject ,Spectral graph theory ,Applied Mathematics ,lcsh:Mathematics ,lcsh:QA1-939 ,Combinatorics ,Graph energy ,Estrada index ,Folded cube graph ,Hypercube ,Adjacency matrix ,Analysis ,Eigenvalues and eigenvectors ,Mathematics ,Characteristic polynomial - Abstract
LetGbe a simple graph withnvertices and letλ1,λ2,…,λnbe the eigenvalues of its adjacency matrix; the Estrada indexEEGof the graphGis defined as the sum of the termseλi, i=1,2,…,n. Then-dimensional folded hypercube networksFQnare an important and attractive variant of then-dimensional hypercube networksQn, which are obtained fromQnby adding an edge between any pair of vertices complementary edges. In this paper, we establish the explicit formulae for calculating the Estrada index of the folded hypercubes networksFQnby deducing the characteristic polynomial of the adjacency matrix in spectral graph theory. Moreover, some lower and upper bounds for the Estrada index of the folded hypercubes networksFQnare proposed.
- Published
- 2014
29. Moving Vehicle Tracking Optimization Method Based on SPF.
- Author
-
Lv, Caixia and Zhang, Xuejing
- Subjects
ARTIFICIAL satellite tracking ,BEHAVIORAL assessment ,INTELLIGENT transportation systems ,TRAFFIC police ,BIPARTITE graphs ,UNDIRECTED graphs - Abstract
In the intelligent transportation system, the license information can be automatically recognized by the computer and the vehicle can be tracked. Red light running, illegal change of lanes, vehicle retrograde, and other illegal driving events are reasonably recorded. This is undoubtedly an effective help for the traffic police to relieve the huge work pressure. However, in China, a considerable number of vehicle tracking methods have certain limitations in resisting complex external environmental influences. The external environmental factors include but not limited to variable factors such as camera movement, jitter, and severe rain and snow. These factors cannot be controlled well, so the tracking accuracy is greatly reduced. In regard to this, this paper proposes an optimization method for moving vehicle tracking based on SPF. First, according to the size of the overlapping area of the motion area between the two images, the researcher can construct and simplify the vertex adjacency matrix that reflects the characteristics of the undirected bipartite graph. Then according to the corresponding relationship between the vertex adjacency matrix and the regional behavior and vehicle behavior, the researcher completes the regional behavior analysis and vehicle behavior analysis. On this basis, a particle filter vehicle tracking algorithm based on segmentation compensation is introduced, and the vector sum of the tracked segmentation area is used as the final position of the target vehicle. In this way, as many scattered particles fall on the target area as possible, which will greatly improve the efficiency of particle utilization, enhance tracking accuracy, and avoid the problem of tracking failure caused by too fast vehicle movement. Through experimental simulation, it can be seen that the method proposed in this paper can greatly enhance the vehicle tracking ability when tracking vehicles in "complex environments." [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
30. Transmit Power Allocation with Connectivity Probability for Multi-QoS in Cluster Flight Spacecraft Network.
- Author
-
Mo, Jinrong, Hu, Shengbo, Yan, Tingting, Song, Xiaowei, and Shi, Yanfeng
- Subjects
MONTE Carlo method ,FLIGHT ,ERROR rates ,PROBABILISTIC number theory ,SPACE vehicles - Abstract
In this paper, we investigate the transmit power allocation problem to minimize the average packet error rate at the access point in the cluster flight spacecraft network, which adopts the CSMA/CA channel access mechanism. First, the node mobility, nodal distance distribution, and probabilistic adjacency matrix were formulated for cluster flight spacecraft network based on twin-satellite mode. Then, the optimization-theoretic model described the optimized transmit power allocation strategy and its implementation algorithm was proposed. And the problem of minimizing the packet error rate of the cluster flight spacecraft network system can be converted into maximizing the expectation of the binary probabilistic adjacency matrix, i.e., maximizing the sum of the nondiagonal elements in the probabilistic adjacency matrix. Due to discreteness of nodal distance distribution, Monte Carlo method was applied to solve the transmit power allocation problem. Yet importantly, the influence of node transmit power on the QoS performance of cluster flight spacecraft network was simulated and analyzed under the assumption of finite overall network transmit power and low traffic load. Finally, the results show that the pocket error rate increases with the provided traffic load, but the pocket error rate hardly changes with the same traffic load in different sequential time slots of any orbital hyperperiod or in the same time slot of different orbital hyperperiods, and by maximizing the sum of the nondiagonal elements in the probabilistic adjacency matrix, the pocket error rate minimum is achieved for a given total network transmit power at any time slot for cluster flight spacecraft network. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
31. Computing the Energy and Estrada Index of Different Molecular Structures.
- Author
-
Mufti, Zeeshan Saleem, Anjum, Rukhshanda, Xin, Qin, Tchier, Fairouz, Anwar-ul-Haq, Iram, and Gaba, Yaé Ulrich
- Subjects
MOLECULAR structure ,EIGENVALUES ,NAPHTHALENE - Abstract
Graph energy is an invariant that is derived from the spectrum of the adjacency matrix of a graph. Graph energy is actually the absolute sum of all the eigenvalues of the adjacency matrix of a graph i.e. E = ∑ i = 1 n λ i , and the Estrada index of a graph G is elaborated as EE G = ∑ i = 1 n e λ i , where, λ 1 , λ 2 , ... , λ n are the eigenvalues of the adjacency matrix of a graph. In this paper, energy E G and Estrada index EE G of different molecular structures are obtained and also established inequalities among the exact and estimated values of energies and Estrada index of TUC 4 C 8 nanosheet and naphthalene. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
32. The complete product of annihilatingly unique digraphs
- Author
-
Che-Sheng Gan
- Subjects
Discrete mathematics ,Combinatorics ,Minimal polynomial (field theory) ,Polynomial ,Mathematics (miscellaneous) ,Degree (graph theory) ,Product (mathematics) ,lcsh:Mathematics ,Digraph ,Adjacency matrix ,lcsh:QA1-939 ,Monic polynomial ,Mathematics - Abstract
LetGbe a digraph withnvertices and letA(G)be its adjacency matrix. A monic polynomialf(x)of degree at mostnis called an annihilating polynomial ofGiff(A(G))=0.Gis said to be annihilatingly unique if it possesses a unique annihilating polynomial. Difans and diwheels are two classes of annihilatingly unique digraphs. In this paper, it is shown that the complete product of difan and diwheel is annihilatingly unique.
- Published
- 2005
33. Hamiltonicity in Directed Toeplitz Graphs with s1=1 and s3=4.
- Author
-
Malik, Shabnam
- Subjects
TOEPLITZ matrices ,TOEPLITZ operators ,HAMILTONIAN coordinates ,HAMILTONIAN graph theory ,HAMILTON'S equations - Abstract
A directed Toeplitz graph T n s 1 , ⋯ , s k ; t 1 , ⋯ , t l with vertices 1 , 2 , ⋯ , n is a directed graph whose adjacency matrix is a Toeplitz matrix. In this paper, we investigate the Hamiltonicity in directed Toeplitz graphs T n s 1 , ⋯ , s k ; t 1 , ⋯ , t l with s 1 = 1 and s 3 = 4. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
34. CPT-DF: Congestion Prediction on Toll-Gates Using Deep Learning and Fuzzy Evaluation for Freeway Network in China.
- Author
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Shi, Tongtong, Wang, Ping, Qi, Xudong, Yang, Jiacheng, He, Rui, Yang, Jingwen, and Han, Yu
- Subjects
FUZZY algorithms ,DEEP learning ,ARTIFICIAL intelligence ,TRAFFIC congestion ,TRAFFIC flow ,TOLLS ,SPATIOTEMPORAL processes ,FEATURE extraction ,EXPRESS highways - Abstract
Toll-gates are crucial points of management and key congestion bottleneck for the freeway. In order to avoid traffic deterioration and alleviate traffic congestion in advance, it is necessary to predict and evaluate the congestion in toll-gates scattering in large-scale region of freeway network. In this paper, traffic volume and operational delay time are selected from various traffic indicators to evaluate congestion considering the particular characteristics of the traffic flow within the toll-gate area. The congestion prediction method is designed including two modules: a deep learning (DL) prediction and a fuzzy evaluation. We propose a modified deep learning method based on graph convolutional network (GCN) structure in the fusion of dilated causal mechanism and optimize the method for spatial feature extraction by constructing a new adjacency matrix. This new AI network could process spatiotemporal information of traffic volume and operational delay time, that extracted from large-scaled toll-gates spontaneously, and predict key indicators in 15/30/60 min future time. The evaluation module is proposed based on these predicted results. Then, fuzzy C-means algorithm (FCM) is further modified by determining coupling weight for these two key indicators to detect congestion state. Original traffic data are obtained from the current 186 toll-gates served on the freeway network in Shaanxi Province, China. Experimental tests are carried out based on historical data of four months after preprogressing. The comparative tests show the proposed CPT-DF (congestion prediction on toll-gates using deep learning and fuzzy evaluation) outperforms the current-used other models by 6-15%. The successful prediction could extend to the real-time prediction and early warning of traffic congestion in the toll system to improve the intelligent level of traffic emergency management and guidance on the key road of disasters to some extent. [ABSTRACT FROM AUTHOR]
- Published
- 2023
- Full Text
- View/download PDF
35. Group-Based Susceptible-Infectious-Susceptible Model in Large-Scale Directed Networks.
- Author
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Zheng, Kangfeng, Wang, Xu, Niu, Xinxin, Liu, Ren Ping, Guo, Y. Jay, Song, Bo, and Ni, Wei
- Subjects
MARKOV processes ,EPIDEMIOLOGICAL models ,TOPOLOGY ,DIRECTORY services (Computer network technology) ,JACOBIAN matrices - Abstract
Epidemic models trade the modeling accuracy for complexity reduction. This paper proposes to group vertices in directed graphs based on connectivity and carries out epidemic spread analysis on the group basis, thereby substantially reducing the modeling complexity while preserving the modeling accuracy. A group-based continuous-time Markov SIS model is developed. The adjacency matrix of the network is also collapsed according to the grouping, to evaluate the Jacobian matrix of the group-based continuous-time Markov model. By adopting the mean-field approximation on the groups of nodes and links, the model complexity is significantly reduced as compared with previous topological epidemic models. An epidemic threshold is deduced based on the spectral radius of the collapsed adjacency matrix. The epidemic threshold is proved to be dependent on network structure and interdependent of the network scale. Simulation results validate the analytical epidemic threshold and confirm the asymptotical accuracy of the proposed epidemic model. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
36. Feature Extraction of Sequence of Keystrokes in Fixed Text Using the Multivariate Hawkes Process.
- Author
-
Zhang, Chang, Zhang, Yuchen, and Li, Fulin
- Subjects
FEATURE extraction ,VECTOR valued functions ,TEXT recognition - Abstract
In this paper, we propose a new method of extracting the features of keystrokes. The Hawkes process based on exponential excitation kernel was used to model the sequence of keystrokes in fixed text, and the intensity function vector and adjacency matrix of the model obtained through training were regarded as the characteristics of the keystrokes. A visual analysis was carried out on the CMU keystroke raw data and the feature data extracted using the proposed method. We used one-class classifier to compare the classification effect of CMU keystroke raw data and the feature data extracted by the Hawkes process model and POHMM model. The experimental results show that the feature data extracted using the proposed method contains rich information to distinguish users. In addition, the feature data extracted using the proposed method has a slightly better classification performance than the original CMU keystroke data for some users who are not easy to distinguish. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
37. Dimension Reduction Using Samples’ Inner Structure Based Graph for Face Recognition
- Author
-
Zhezhou Yu, Bin Li, Xiangchun Yu, Yecheng Zhang, Yuhao Liu, Anan Du, and Wei Pang
- Subjects
Theoretical computer science ,Article Subject ,Graph embedding ,business.industry ,General Mathematics ,lcsh:Mathematics ,General Engineering ,Voltage graph ,Pattern recognition ,Strength of a graph ,lcsh:QA1-939 ,Geometric graph theory ,Graph power ,lcsh:TA1-2040 ,Graph (abstract data type) ,Adjacency matrix ,Artificial intelligence ,Lattice graph ,business ,lcsh:Engineering (General). Civil engineering (General) ,Mathematics - Abstract
Graph construction plays a vital role in improving the performance of graph-based dimension reduction (DR) algorithms. In this paper, we propose a novel graph construction method, and we name the graph constructed from such method as samples’ inner structure based graph (SISG). Instead of determining thek-nearest neighbors of each sample by calculating the Euclidean distance between vectorized sample pairs, our new method employs the newly defined sample similarities to calculate the neighbors of each sample, and the newly defined sample similarities are based on the samples’ inner structure information. The SISG not only reveals the inner structure information of the original sample matrix, but also avoids predefining the parameterkas used in thek-nearest neighbor method. In order to demonstrate the effectiveness of SISG, we apply it to an unsupervised DR algorithm, locality preserving projection (LPP). Experimental results on several benchmark face databases verify the feasibility and effectiveness of SISG.
- Published
- 2014
38. Permanents of Hexagonal and Armchair Chains.
- Author
-
Nekooei, O., Barzegar, H., and Ashrafi, A. R.
- Subjects
ARMCHAIRS ,SIGNS & symbols ,PHYSICS ,CAYLEY graphs - Abstract
The permanent is important invariants of a graph with some applications in physics. If G is a graph with adjacency matrix A = a i j , then the permanent of A is defined as perm A = ∑ σ ∈ S n ∏ i = 1 n a i σ i , where S n denotes the symmetric group on n symbols. In this paper, the general form of the adjacency matrices of hexagonal and armchair chains will be computed. As a consequence of our work, it is proved that if G k and H k denote the hexagonal and armchair chains, respectively, then perm A G 1 = 4 , perm A G k = k + 1 2 , k ≥ 2 , and perm A H k = 4 k with k ≥ 1. One question about the permanent of a hexagonal zig-zag chain is also presented. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
39. On the Alexander polynominals of alternating two-component links
- Author
-
Mark E. Kidwell
- Subjects
alternating link projection ,Alexander matrix and polynomial ,adjacency matrix ,rooted tree. ,Mathematics ,QA1-939 - Abstract
Let L be an alternating two-component link with Alexander polynomial Δ(x,y). Then the polynomials (1−x)Δ(x,y) and (1−y)Δ(x,y) are alternating. That is, (1−y)Δ(x,y) can be written as ∑i,jcijxiyj in such a way that (−1)i+jcij≥0.
- Published
- 1979
- Full Text
- View/download PDF
40. Semisupervised Community Preserving Network Embedding with Pairwise Constraints.
- Author
-
Liu, Dong, Ru, Yan, Li, Qinpeng, Wang, Shibin, and Niu, Jianwei
- Subjects
MATRIX decomposition ,MACHINE learning ,VIRTUAL networks ,NONNEGATIVE matrices ,TASK analysis ,COMMUNITIES ,FORECASTING - Abstract
Network embedding aims to learn the low-dimensional representations of nodes in networks. It preserves the structure and internal attributes of the networks while representing nodes as low-dimensional dense real-valued vectors. These vectors are used as inputs of machine learning algorithms for network analysis tasks such as node clustering, classification, link prediction, and network visualization. The network embedding algorithms, which considered the community structure, impose a higher level of constraint on the similarity of nodes, and they make the learned node embedding results more discriminative. However, the existing network representation learning algorithms are mostly unsupervised models; the pairwise constraint information, which represents community membership, is not effectively utilized to obtain node embedding results that are more consistent with prior knowledge. This paper proposes a semisupervised modularized nonnegative matrix factorization model, SMNMF, while preserving the community structure for network embedding; the pairwise constraints (must-link and cannot-link) information are effectively fused with the adjacency matrix and node similarity matrix of the network so that the node representations learned by the model are more interpretable. Experimental results on eight real network datasets show that, comparing with the representative network embedding methods, the node representations learned after incorporating the pairwise constraints can obtain higher accuracy in node clustering task and the results of link prediction, and network visualization tasks indicate that the semisupervised model SMNMF is more discriminative than unsupervised ones. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
41. Internal Resource Allocation to Information Security of Smart Cities Using Evolutionary Game Model.
- Author
-
Li, Jun, Zou, Kai, and Xing, Lining
- Subjects
SMART cities ,INFORMATION technology security ,EVOLUTIONARY models ,RESOURCE allocation ,INFORMATION resources - Abstract
The internal resource allocation approach is proposed for the information security of smart cities using the evolutionary game model. Focusing on the information resource allocation of smart cities, the relationship among information security factors is first analyzed, then the directed connection graph and adjacency matrix are constructed to obtain its directed hierarchical structure diagram by calculating the reachable matrix, thereby developing an explanatory structure model for information security resources in smart cities. Furthermore, an evolutionary game model is established, and the replicator dynamics are used to adjust the model. Finally, three types of typical problems are investigated through simulation experiments to verify and explain the structural model. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
42. Organizational Construction of Financial Management Application Platform Based on Commercial Random Matrix.
- Author
-
Tao, Yunqiu, Wang, Wanxin, and Yang, Wei
- Subjects
RANDOM matrices ,FINANCIAL management ,CONSTRUCTION management ,FINANCIAL management software ,BUDGET management - Abstract
As one of the basic tools of statistical analysis, random matrix theory builds a bridge for the study of microscopic and macroscopic properties of complex networks. The eigenvalues of the adjacency matrix of the complex network after mapping correspond to the energy spectrum in the random matrix theory, so the characteristics of the complex network are concentrated in the volatility of the eigenvalue sequence. The eigenvalues of complex networks are analyzed through random matrix theory to find the relationship between their structure and properties. This paper discusses the design and realization of the comprehensive application platform of group enterprise financial management. Based on the current situation of the development of financial management software in domestic and foreign group companies, the thesis gives the goals and tasks of the platform research and design. The enterprise financial management platform has made a business and technical architecture design and has made an in-depth analysis and design of modules such as fund management, budget management, statement management, and accounting management. This paper finds that the critical point threshold makes the results of network community division below this threshold poor, and the results above this threshold are good. Furthermore, the tipping point threshold for the NYSE network is significantly lower than that of the CSM network. This paper further analyzes the dynamic cross-correlation matrix and examines the time-dependent changes in the number of outliers, the number of communities, the degree of network modularity, and the degree of decentralization of industry composition under the condition of two market critical point thresholds. From the magnitude of fluctuation, it is found that the NYSE network is much more stable than the CSM network, which may also be related to the basic characteristics of the two markets, because the capital chain relationship is much more stable than the supply chain relationship. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
- View/download PDF
43. Nordhaus–Gaddum-Type Relations for Arithmetic-Geometric Spectral Radius and Energy.
- Author
-
Wang, Yajing and Gao, Yubin
- Subjects
ARITHMETIC mean ,GRAPH theory ,SPECTRAL theory ,RADIUS (Geometry) - Abstract
Spectral graph theory plays an important role in engineering. Let G be a simple graph of order n with vertex set V = v 1 , v 2 , ... , v n . For v i ∈ V , the degree of the vertex v i , denoted by d i , is the number of the vertices adjacent to v i . The arithmetic-geometric adjacency matrix A a g G of G is defined as the n × n matrix whose i , j entry is equal to d i + d j / 2 d i d j if the vertices v i and v j are adjacent and 0 otherwise. The arithmetic-geometric spectral radius and arithmetic-geometric energy of G are the spectral radius and energy of its arithmetic-geometric adjacency matrix, respectively. In this paper, some new upper bounds on arithmetic-geometric energy are obtained. In addition, we present the Nordhaus–Gaddum-type relations for arithmetic-geometric spectral radius and arithmetic-geometric energy and characterize corresponding extremal graphs. [ABSTRACT FROM AUTHOR]
- Published
- 2020
- Full Text
- View/download PDF
44. Software Knowledge Entity Relation Extraction with Entity-Aware and Syntactic Dependency Structure Information.
- Author
-
Tang, Mingjing, Li, Tong, Wang, Wei, Zhu, Rui, Ma, Zifei, and Tang, Yahui
- Subjects
KNOWLEDGE graphs ,COMPUTER software ,USER-generated content ,CONTEXTUAL learning - Abstract
Software knowledge community contains a large scale of software knowledge entities with complex structure and rich semantic relations. Semantic relation extraction of software knowledge entities is a critical task for software knowledge graph construction, which has an important impact on knowledge graph based tasks such as software document generation and software expert recommendation. Due to the problems of entity sparsity, relation ambiguity, and the lack of annotated dataset in user-generated content of software knowledge community, it is difficult to apply existing methods of relation extraction in the software knowledge domain. To address these issues, we propose a novel software knowledge entity relation extraction model which incorporates entity-aware information with syntactic dependency information. Bidirectional Gated Recurrent Unit (Bi-GRU) and Graph Convolutional Networks (GCN) are used to learn the features of contextual semantic representation and syntactic dependency representation, respectively. To obtain more syntactic dependency information, a weight graph convolutional network based on Newton's cooling law is constructed by calculating a weight adjacency matrix. Specifically, an entity-aware attention mechanism is proposed to integrate the entity information and syntactic dependency information to improve the prediction performance of the model. Experiments are conducted on a dataset which is constructed based on texts of the StackOverflow and show that the proposed model has better performance than the benchmark models. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
45. An Improved Ant Colony Algorithm of Robot Path Planning for Obstacle Avoidance.
- Author
-
Wang, Hong-Jun, Fu, Yong, Zhao, Zhuo-Qun, and Yue, You-Jun
- Abstract
The obstacle avoidance in path planning, a hot topic in mobile robot control, has been extensively investigated. The existing ant colony algorithms, however, remain as drawbacks including failing to cope with narrow aisles in working areas, large amount of calculation, etc. To address above technical issues, an improved ant colony algorithm is proposed for path planning. In this paper, a new weighted adjacency matrix is presented to determine the walking direction and the narrow aisles therefore are avoided by redesigning the walking rules. Also, the best ant and the worst ant are introduced for the adjustment of pheromone to facilitate the searching process. The proposed algorithm guarantees that robots are able to find a satisfying path in the presence of narrow aisles. The simulation results show the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
46. PM2.5 Concentration Forecasting in Industrial Parks Based on Attention Mechanism Spatiotemporal Graph Convolutional Networks.
- Author
-
Zeng, Qingtian, Wang, Chao, Chen, Geng, and Duan, Hua
- Subjects
INDUSTRIAL districts ,INDUSTRIAL concentration ,DEEP learning ,STANDARD deviations ,CONVOLUTIONAL neural networks ,EUCLIDEAN distance - Abstract
Industrial parks are one of the main sources of air pollution; the ability to forecast PM2.5, the main pollutant in the industrial park, is of great significance to the health of the workers in the industrial park and environmental governance, which can improve the decision-making ability of environmental management. Most of the existing PM2.5 concentration forecast methods lack the ability to model the dynamic temporal and spatial correlations of PM2.5 concentration. In an industrial park environment, in order to improve the accuracy of PM2.5 concentration forecast, based on deep learning technology, this paper proposes a spatiotemporal graph convolutional network based on the attention mechanism (STAM-STGCN) to solve the PM2.5 concentration forecast problem. When constructing the adjacency matrix, we not only use the Euclidean distance between sites but also consider the impact of wind fields and the impact of pollution sources near the nodes. In the process of model construction, we first use the spatiotemporal attention mechanism to capture the dynamic spatiotemporal correlations in PM2.5 data. In the spatiotemporal convolution module, we use graph convolutional neural networks to capture spatial features and standard convolution to describe temporal features. Finally, the output module adjusts the output shape of the data to produce the final forecast result. In this paper, the mean absolute error (MAE), root mean squared error (RMSE), and mean absolute percentage error (MAPE) are used as the performance evaluation metrics of the model, and the Dongmingnan Industrial Park atmospheric dataset is used to verify the effectiveness of the proposed algorithm. The experimental results show that our STAM-STGCN model can more fully capture the spatial-temporal characteristics of PM2.5 concentration data; compared with the most advanced model in the comparison model, the RMSE can be improved about 24.2%, the MAE is improved about 35.8%, and the MAPE is improved about 34.6%. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
47. DEMLP: DeepWalk Embedding in MLP for miRNA-Disease Association Prediction.
- Author
-
Wang, Xun, Wang, Fuyu, Wang, Xinzeng, Qiao, Sibo, and Zhuang, Yu
- Subjects
MICRORNA ,FORECASTING ,NEIGHBORHOODS ,COST ,COMPUTERS - Abstract
miRNAs significantly affect multifarious biological processes involving human disease. Biological experiments always need enormous financial support and time cost. Taking expense and difficulty into consideration, to predict the potential miRNA-disease associations, a lot of high-efficiency computational methods by computer have been developed, based on a network generated by miRNA-disease association dataset. However, there exist many challenges. Firstly, the association between miRNAs and diseases is intricate. These methods should consider the influence of the neighborhoods of each node from the network. Secondly, how to measure whether there is an association between two nodes of the network is also an important problem. In our study, we innovatively integrate graph node embedding with a multilayer perceptron and propose a method DEMLP. To begin with, we construct a miRNA-disease network by miRNA-disease adjacency matrix (MDA). Then, low-dimensional embedding representation vectors of nodes are learned from the miRNA-disease network by DeepWalk. Finally, we use these low-dimensional embedding representation vectors as input to train the multilayer perceptron. Experiments show that our proposed method that only utilized the miRNA–disease association information can effectively predict miRNA-disease associations. To evaluate the effectiveness of DEMLP in a miRNA-disease network from HMDD v3.2, we apply fivefold crossvalidation in our study. The ROC-AUC computed result value of DEMLP is 0.943, and the PR-AUC value of DEMLP is 0.937. Compared with other state-of-the-art methods, our method shows good performance using only the miRNA-disease interaction network. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
48. GCNRDM: A Social Network Rumor Detection Method Based on Graph Convolutional Network in Mobile Computing.
- Author
-
Xu, Dawei, Liu, Qing, Zhu, Liehuang, Tan, Zhonghua, Gao, Feng, and Zhao, Jian
- Subjects
SOCIAL networks ,MOBILE computing ,DATA transmission systems ,RUMOR ,DEEP learning ,TELECOMMUNICATION systems ,INFORMATION networks - Abstract
Mobile computing is a new technology emerging with the development of mobile communication, Internet, database, distributed computing, and other technologies. Mobile computing technology will enable computers or other information intelligent terminal devices to realize data transmission and resource sharing in the wireless environment. Its role is to bring useful, accurate, and timely information to any customer at anytime, anywhere, and to change the way people live and work. In mobile computing environment, a lot of Internet rumors hidden among the huge amounts of information communication network can cause harm to society and people's life; this paper proposes a model of social network rumor detection based on convolution networks, the use of adjacency matrix between the nodes represent user and the relationship between the constructions of social network topology. We use a high-order graph neural network (K-GNN) to extract the rumor posting features. At the same time, the graph attention network (GAT) is used to extract the association features of other nodes of the network topology. The experimental results show that the method of the detection model in this paper improves the accuracy of prediction classification compared with deep learning methods such as RNN, GRU, and attention mechanism. The innovation of the paper proposes a rumor detection model based on the graph convolutional network, which lies in considering the propagation structure among users. It has a strong practical value. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
49. Decoding of Motor Coordination Imagery Involving the Lower Limbs by the EEG-Based Brain Network.
- Author
-
Fu, Yunfa, Zhou, Zhouzhou, Gong, Anmin, Qian, Qian, Su, Lei, and Zhao, Lei
- Subjects
MOTOR ability ,SUPPORT vector machines ,LEG ,HEMIPLEGICS ,TIME-frequency analysis ,TIME-domain analysis - Abstract
Compared with the efficacy of traditional physical therapy, a new therapy utilizing motor imagery can induce brain plasticity and allows partial recovery of motor ability in patients with hemiplegia after stroke. Here, we proposed an updated paradigm utilizing motor coordination imagery involving the lower limbs (normal gait imagery and hemiplegic gait imagery after stroke) and decoded such imagery via an electroencephalogram- (EEG-) based brain network. Thirty subjects were recruited to collect EEGs during motor coordination imagery involving the lower limbs. Time-domain analysis, power spectrum analysis, time-frequency analysis, brain network analysis, and statistical analysis were used to explore the neural mechanisms of motor coordination imagery involving the lower limbs. Then, EEG-based brain network features were extracted, and a support vector machine was used for decoding. The results showed that the two employed motor coordination imageries mainly activated sensorimotor areas; the frequency band power was mainly concentrated within theta and alpha bands, and brain functional connections mainly occurred in the right forehead. The combination of the network attributes of the EEG-based brain network and the spatial features of the adjacency matrix had good separability for the two kinds of gait imagery (p < 0.05), and the average classification accuracy of the combination feature was 92.96% ± 7.54%. Taken together, our findings suggest that brain network features can be used to identify normal gait imagery and hemiplegic gait imagery after stroke. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
- View/download PDF
50. A theoretical foundation for the relationship between generalized origin--destination matrix and flow matrix based on ordinal graph trajectories.
- Author
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Teknomo, Kardi and Fernandez, Proceso
- Subjects
ORIGIN & destination traffic surveys ,ORDINAL numbers ,ORDINAL measurement ,TRAFFIC flow measurement ,TRAFFIC surveys - Abstract
This paper shows the relationship between flow, generalized origin-destination (OD), and alternative route flow from a set of ordinal graph trajectories. In contrast to traffic assignment methods that employ OD matrix to produce flow matrix, we use ordinal trajectory on a network graph as input and produce both the generalized OD matrix and the flow matrix, with the alternative and substitute route flow matrices as additional outputs. By using linear algebra-like operations on matrix sets, the relationship between network utilization (in terms of flow, generalized OD, alternative route flow, and desire line) and network structure (in terms of distance matrix and adjacency matrix) are derived. [ABSTRACT FROM AUTHOR]
- Published
- 2014
- Full Text
- View/download PDF
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