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One of the modern directions of obtaining information for making informed decisions is the analysis of data from open Internet sources, the analysis of media containing hundreds of thousands of publications. It is critically important not only to obtain reliable information, but also the time needed to obtain and analyze it. The purpose of the research in this work is the development and testing of a complex methodology for quickly building a prototype of a system for complex analysis of thematic sites. A technology of interconnected methods, methodologies, and tools for building a graph database, a knowledge graph, data analysis using methods and mo dels of machine learning with the provision of analytical results to users has been created. The main task of this work is to use these technologies to analyze data from well-known world sites in order to build a prototype of a systems for complex analysis of data from Internet sources.

Currently, high-performance computing environments are facing challenges such as limited resources and an increasing number of users. In order to improve the utilization of environmental resources, this paper proposes a high-performance hybrid computing architecture based on big data processing technology, which is constructed on the basis of an HDFS distributed system combined with MapReduce framework and GPU virtualization technology. The PageRank algorithm is utilized to evaluate the performance of rack nodes in the high-performance computing environment, and the evaluation results are applied to design an improvement strategy for task allocation and scheduling through the MapReduce framework. A division function is introduced to dynamically divide the Reduce data, and an approximate sampling method based on sampling information is proposed to guide the setting of the number of Reduce. The IB algorithm is used to cluster the labeled files, and a rack-aware strategy is designed based on HDFS to achieve resource load balancing. The MapReduce-based task allocation scheduling scheme has a reduction in job execution time of up to 39.83% compared to delayed scheduling. The dynamic partitioning design can achieve data load balancing by partitioning 5.382% of the groups and migrating 1.207% of the KVs if the data skew is 1.0. Dynamic balancing of environmental resources and resource scheduling optimization in high-performance computing environments can be achieved through the use of big data processing techniques.

In the digital era, the proliferation of new media, facilitated by digital platforms, has positioned new media marketing as a predominant marketing strategy. This study introduces a novel node ranking algorithm, leveraging the principles of PageRank, and proposes the UIEM-CMR influence assessment model derived from it. Utilizing data extracted from microblogging platforms and Facebook, this model evaluates the influence of various new media marketing nodes. Subsequently, the ten most influential nodes within the microblogging dataset were analyzed to examine the correlation between node influence in new media marketing and both the thematic distribution of new media marketing and the community distribution of followers. The findings reveal that the MR value of node influence, as calculated by the model, is impacted by the combined effects of AR and BR values, with these values alone identified as pivotal. The influence ranking, when solely based on AR and BR values, tends to diverge significantly from real-world scenarios. Nodes ranked highly under popular new media marketing topics exhibit a substantially greater probability of distribution compared to lower-ranked or randomly chosen nodes. Conversely, the distribution probabilities among influential nodes under less popular (cold) new media marketing topics show no significant disparity. Nodes with high influence ratings tend to attract followers from at least two distinct communities. The UIEM-CMR model, as developed in this study, proves to be an effective tool for assessing the influence of new media marketing strategies.

Creating policy measures is the final step in the process of e-learning roadmap development. Policy measures can be seen as long-term activities that need to be implemented and constantly upgraded to achieve strategic goals. For resource allocation, it is useful to prioritize policy measures. Prioritization can be implemented using multi-criteria decision-making methods. This paper analyzes policy measures in the Maldives National University's e-learning roadmap using the social network analysis process (SNAP), which includes the analytic hierarchy process (AHP), the decision-making trial and evaluation laboratory (DEMATEL), and the PageRank centrality. In policy measure evaluation, there were more than 20 participants: persons with managerial functions at the Maldives National University (MNU) (deans, heads of departments) and persons in lecturer and researcher positions. By using the AHP, participants prioritized policy measures with respect to their importance to them. By using the DEMATEL, participants identified and prioritized policy measures with respect to their effect on other measures. Finally, by using the SNAP, it was possible to determine the prioritization list for resource allocation since it aggregates the aspects of the policy measures, their importance, and their effect on other measures. [ABSTRACT FROM AUTHOR]

Back in 1988, the partial game trees explored by computer chess programs were among the largest search structures in real-world computing. Because the game tree is too large to be fully evaluated, chess programs must make heuristic strategic decisions based on partial information, making it an illustrative subject for teaching AI search. In one of his lectures that year on AI search for games and puzzles, Professor Hans Berliner—a pioneer of computer chess programs1—stated: "Intelligent heuristics are the future of computing." As a student in the field of the theory of computation, I was naturally perplexed but fascinated by this perspective. I had been trained to believe that "Algorithms and computational complexity theory are the foundation of computer science." However, as it happens, my attempts to understand heuristics in computing have subsequently played a significant role in my career as a theoretical computer scientist. I have come to realize that Berliner's postulation is a far-reaching worldview, particularly in the age of big, rich, complex, and multifaceted data and models, when computing has ubiquitous interactions with science, engineering, humanity, and society. In this article,2I will share some of my experiences on the subject of heuristics in computing, presenting examples of theoretical attempts to understand the behavior of heuristics on real data, as well as efforts to design practical heuristics with desirable theoretical characterizations. My hope is that these theoretical insights from past heuristics—such as spectral partitioning, multilevel methods, evolutionary algorithms, and simplex methods—can shed light on and further inspire a deeper understanding of the current and future techniques in AI and data mining. [ABSTRACT FROM AUTHOR]

There are errors in multi-source uncertain time series data. Truth discovery methods for time series data are effective in finding more accurate values, but some have limitations in their usability. To tackle this challenge, we propose a new and convenient truth discovery method to handle time series data. A more accurate sample is closer to the truth and, consequently, to other accurate samples. Because the mutual-confirm relationship between sensors is very similar to the mutual-quote relationship between web pages, we evaluate sensor reliability based on PageRank and then estimate the truth by sensor reliability. Therefore, this method does not rely on smoothness assumptions or prior knowledge of the data. Finally, we validate the effectiveness and efficiency of the proposed method on real-world and synthetic data sets, respectively. [ABSTRACT FROM AUTHOR]

Image sentiment analysis technology can predict, measure and understand the emotional experience of human beings through images. Aiming at the problem of extracting emotional characteristics in art appreciation, this article puts forward an innovative method. Firstly, the PageRank algorithm is enhanced using tweet content similarity and time factors; secondly, the SE-ResNet network design is used to integrate Efficient Channel Attention (ECA) with the residual network structure, and ResNeXt50 is optimized to enhance the extraction of image sentiment features. Finally, the weight coefficients of overall emotions are dynamically adjusted to select a specific emotion incorporation strategy, resulting in effective bimodal fusion. The proposed model demonstrates exceptional performance in predicting sentiment labels, with maximum classification accuracy reaching 88.20%. The accuracy improvement of 21.34% compared to the traditional deep convolutional neural networks (DCNN) model attests to the effectiveness of this study. This research enriches images and texts' emotion feature extraction capabilities and improves the accuracy of emotion fusion classification. [ABSTRACT FROM AUTHOR]

Ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including internet ranking, scientometrics, and systemic risk in finance (SinkRank and DebtRank). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites for search engine results based on linear algebra rules. But how robust is this method in times of rapid internet growth? Recent works have studied robust reformulations of the PageRank model for the case when links in the network structure may vary; that is, some links may appear or disappear, influencing the transportation matrix defined by the network structure. In this article, the authors make a further step forward, allowing the network to vary not only in links but also in the number of nodes. The authors focus on growing network structures and develop methods for ranking of networks uncertain both in size and in structure. Internet ranking algorithms play a crucial role in information technologies and numerical analysis due to their efficiency in high dimensions and wide range of possible applications, including scientometrics and systemic risk in finance (SinkRank, DebtRank, etc.). The traditional approach to internet ranking goes back to the seminal work of Sergey Brin and Larry Page, who developed the initial method PageRank (PR) in order to rank websites in search engine results. Recent works have studied robust reformulations of the PageRank model for the case when links in the network structure may vary; that is, some links may appear or disappear influencing the transportation matrix defined by the network structure. We make a further step forward, allowing the network to vary not only in links, but also in the number of nodes. We focus on growing network structures and propose a new robust formulation of the PageRank problem for uncertain networks with fixed growth rate. Defining the robust PageRank in terms of a nonconvex optimization problem, we bound our formulation from above by a convex but nonsmooth optimization problem. Driven by the approximation quality, we analyze the resulting optimality gap theoretically and demonstrate cases for its reduction. In the numerical part of the article, we propose some techniques which allow us to obtain the solution efficiently for middle-size networks avoiding all nonsmooth points. Furthermore, we propose a coordinate-wise descent method with near-optimal step size and address high-dimensional cases using multinomial transition probabilities. We analyze the impact of the network growth on ranking and numerically assess the approximation quality using real-world data sets on movie repositories and on journals on computational complexity. Funding: This work was supported by the Austrian Science Fund [Grant J3674-N26]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2022.2298. [ABSTRACT FROM AUTHOR]

Considering that travel agencies will pay attention to the demand information and electronic word-of-mouth(e-WOM)development trend of each alternative hotel when choosing partners, a method for hotel partner selection is proposed based on both information. First, the directed and weighted graph is constructed, in which the weight of the node is determined based on the demand information. Further, a time weight correction coefficient that considers the horizontal and vertical development trends of the alternatives’ e-WOM information is proposed to calculate the weight of each stage, and the TOWGA operator is used to calculate the attribute performance of each alternative hotel. By comparing the e-WOM attribute performance, the directed edge and its weight are determined. Then, the algorithm for calculating the ranking value of the alternative hotels is given based on PageRank. Finally, an example is used to verify the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

We consider several families of network centrality measures induced by graph kernels, which include some well-known measures and many new ones. The Self-consistency and Bridge axioms, which appeared earlier in the literature, are closely related to certain kernels and one of the families. We obtain a necessary and sufficient condition for Self-consistency, a sufficient condition for the Bridge axiom, indicate specific measures that satisfy these axioms and show that under some additional conditions they are incompatible. PageRank centrality applied to undirected networks violates most conditions under study and has a property that according to some authors is 'hard to imagine' for a centrality measure. We explain this phenomenon. Adopting the Self-consistency or Bridge axiom leads to a drastic reduction in survey time in the culling method designed to select the most appropriate centrality measures. [ABSTRACT FROM AUTHOR]

In the field of deep matching, a large amount of noisy data in Chinese long texts affects the matching effect. Most long-form text matching models use all text data indiscriminately, which results in a large amount of noisy data, and thus the PageRank algorithm is combined with Transformer to filter noise. For sentence-level noise detection, after calculating the overlap rate of words to evaluate the similarity, a sentence-level relationship graph is constructed and filtered by using the PageRank algorithm; for word-level noise detection, based on the attention score in Transformer, a word graph is established, then the PageRank algorithm is executed on graph, combined with self-attention weights, to select keywords to highlight topic relevance, the noisy words are filtered sequentially at different layers in the module, layer by layer. In addition, during the model training, PolyLoss is applied to replace the traditional binary Cross-Entropy loss function, thus reducing the difficulty of hyperparameter tuning. Finally, a better filtering strategy is proposed and experiments are conducted to verify it on two Chinese long-form text matching datasets. The result shows that the matching model based on the noise filtering strategy of this paper can better filter the noise and capture the matching signal more accurately. [ABSTRACT FROM AUTHOR]

Climate risk is one of the key factors affecting the energy industry, and then affects the global energy trade. By adopting a network approach to the global energy trade and based on the data of global energy trade during 1996–2018, the evolution characteristics of the global energy trade network are hereby investigated, and the impact of climate risk on the global energy trade is explored. The results demonstrate that the global energy trade network has scale-free characteristics and significant regional characteristics and that its heterogeneity is weakening on the whole. Climate risk is negatively correlated with the energy trade relationship between countries. The simulation results show that climate risk has a greater impact on the import status of all countries than their export status and that only a few countries have large climate risk spillover effects. In addition, the climate risk spillover effects of countries are closely related to their import statuses. Overall, the present study provides policy suggestions for identifying important energy trading countries under climate risk shock and preventing the negative impact of climate risks on global energy trade. [ABSTRACT FROM AUTHOR]

In this study, we explore how transformer models, which are known for their attention mechanisms, can improve pathogen prediction in pastured poultry farming. By combining farm management practices with microbiome data, our model outperforms traditional prediction methods in terms of the F1 score—an evaluation metric for model performance—thus fulfilling an essential need in predictive microbiology. Additionally, the emphasis is on making our model’s predictions explainable. We introduce a novel approach for identifying feature importance using the model’s attention matrix and the PageRank algorithm, offering insights that enhance our comprehension of established techniques such as DeepLIFT. Our results showcase the efficacy of transformer models in pathogen prediction for food safety and mark a noteworthy contribution to the progress of explainable AI within the biomedical sciences. This study sheds light on the impact of effective farm management practices and highlights the importance of technological advancements in ensuring food safety.

In this paper, a new multi-parameter iterative algorithm is proposed to address the PageRank problem based on the multi-splitting iteration method. The proposed method solves two linear subsystems at each iteration by splitting the coefficient matrix, considering therefore inner and outer iteration to find the approximate solutions of these linear subsystems. It can be shown that the iterative sequence generated by the multi-parameter iterative algorithm finally converges to the PageRank vector when the parameters satisfy certain conditions. Numerical experiments show that the proposed algorithm has better convergence and numerical stability than the existing algorithms. [ABSTRACT FROM AUTHOR]

PageRank is a popular measure of centrality that is often applied to rank nodes in real-world networks. However, in many cases, the notion of teleportation is counterintuitive because it implies that whatever is moving around the network will jump or 'teleport' directly from one node to any other, without considering how far apart the nodes are. To overcome this issue, we propose here a general measure of PageRank centrality whereby the teleportation probabilities depend, in some way, on the distance separating the nodes. We accomplish this by drawing upon recent advances in non-local random walks, which allow the proposed measure to be tailored for various real-world networks and applications. To illustrate the flexibility of the proposed measure and to demonstrate how it differs from PageRank centrality, we present and discuss experimental results for a selection of real-world spatial and social networks, including an air transportation network, a collaboration network and an urban street network. [ABSTRACT FROM AUTHOR]

Abstract We propose a greedy separation algorithm that finds the most fitted candidate among stochastic block models for a network, based on three known approaches. The first approach tests whether the network has one or more than two communities based on the distribution of the largest eigenvalue of the adjacency matrix. The second uses the algorithm to infer the classed label of each node in the network. The third approach ascertains the optimal number of clusters using an information criterion based on the Bayesian information criterion. The algorithm combined with the above approaches can find a suitable candidate from successively generated stochastic block models. However, in the second approach, the estimated labels heavily depend on the initial labels. The collection of the hub node and its neighbors is expected to construct one class with the same label. We find the hub nodes and enhance the initial labels by the PageRank method. We also conduct experiments with real data to evaluate the accuracy of the proposed method by comparing it with Markov chain Monte Carlo methods. The greedy separation algorithm with the PageRank method is preferable to the Monte Carlo-based methods. [ABSTRACT FROM AUTHOR]

Search engine optimization (SEO), the practice of improving website visibility on search engines, faces the considerable challenges posed by the opacity of Google’s relevance ranking algorithm. Attempts at understanding how this algorithm operates have generated a sizeable number of studies in the worlds of both business and academia. Indeed, this research tradition has managed to present strong evidence regarding the participation of certain factors and their relative importance. For instance, there is a widespread consensus that domain authority is one of the key factors in optimizing positioning. This study seeks to determine the reliability of the domain authority scores provided by three leading platforms for SEO professionals: Moz’s Domain Authority, Semrush’s Authority Score, and Ahrefs’ Domain Rating, values obtained using different indices and applying different procedures. We hypothesize that the degree of coincidence is high, allowing us to deduce that the three tools are, therefore, highly reliable. The method of data triangulation is used to compare the values from these three sources. The degree of coincidence is determined using a statistical analysis based on Spearman’s correlation coefficient (rho). The sample of domains analyzed was selected from 61 neutral queries, which provided 16,937 results and a total of 3,151 domains. When examining the tools in pairs, the correlation coefficients obtained were above 0.9 in all cases. The rho coefficient of the global analysis was also 0.9. This confirms our hypothesis and demonstrates that the three platforms can be considered as providing reliable data. These results are clearly relevant given that SEO professionals depend heavily on domain authority values in their work, and the degree of reliability detected ensures that decision-making based on this indicator can be undertaken with confidence. [ABSTRACT FROM AUTHOR]

We study the complexity of local graph-centrality estimation, with the goal of approximating the centrality score of a given target node while exploring only a sublinear number of nodes/arcs of the graph and performing a sublinear number of elementary operations. We develop a technique, which we apply to PageRank and Heat Kernel, for constructing a low-variance score estimator through a local exploration of the graph. We obtain an algorithm that, given any node in any graph of n nodes and m arcs, with probability (1 - \delta ) computes a multiplicative (1\pm \epsilon )-approximation of its score by examining only O\~(min(n 1/2\Delta 1/2 , n 1/2m1/4 )) nodes/arcs, where \Delta is the maximum outdegree of the graph and poly(\epsilon - 1 ) and polylog(\delta - 1 ) factors are omitted for readability. A similar bound holds for computational cost. We also prove a lower bound of \Omega (min(n 1/2\Delta 1/2, n1/3m1/3 )) for both query complexity and computational complexity. Moreover, in the jump-and-crawl graphaccess model, our technique yields a O\~(min(n 1/2\Delta 1/2 , n2/3 ))-queries algorithm; we show that this algorithm is optimal up to a logarithmic factor-in fact, sublogarithmic in the case of PageRank. These are the first algorithms with sublinear worst-case bounds for general directed graphs and any choice of the target node. [ABSTRACT FROM AUTHOR]

Consider chemical elements as a system, we create an undirected chemical network with 99 elements and 1916 edges from Chemspider, a website that provide search engines to collect compounds. Using this network and the network that we used in our previous work with 97 elements and 2198 edges, we found that RootedPageRank, a link prediction tool in complex network, can be used to predict potential binary compounds, because the changing trend of PageRank probability of each element in these networks all follow the periodic law, despite of the difference of scale of these networks. The accuracy test indicates that at least 7 among top 10 predicted compoundss in one network can be verified using the compoundss in the other network or in other chemical database, proving that this method can be used to provide guidance in finding potential binary compounds, suggesting that we can study chemical properties from the view of complex network. [ABSTRACT FROM AUTHOR]

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases. [ABSTRACT FROM AUTHOR]

Abstract With the development of avionics industry, it is difficult for traditional combat equipment node evaluation method to meet our requirements under complex combat system. This paper presents a method of node importance evaluation which is suitable for modern avionics field and can be used for reference in other combat fields. In order to make better use of the different features of the node itself and the different connections between nodes, we use TOPSIS algorithm to model the characteristics of the node itself, and PageRank to measure the interdependence of all nodes. Therefore, a novel node contribution evaluation algorithm based on TOPSIS and PageRank is proposed in this paper. In addition, after the evaluation of node contribution, we found that there was also a functional relationship between the operational information entropy in the whole graph and the contribution of these nodes. On this basis, information entropy evaluation algorithm of the overall combat map is further proposed. After a lot of experiments, the reliability of our algorithm is evaluated on the indexes of the node's destruction-resistant performance and information transfer efficiency. Compared with the traditional universal algorithm, our proposed algorithm shows more interpretable and robust results in the field of avionics.

It is difficult for users to understand the complex cloud product information for product selection. Using this information to recommend satisfactory cloud products is a challenge. Previous studies focused on similar information of users and products while neglecting relevance; therefore, they could not create recommendation approaches that account for functional dependencies among cloud products. To overcome this challenge, this study proposes a cloud product set recommendation model based on a hierarchical knowledge graph (KG) with a pre-post correlation of product functionality. There are two main contributions: First, we constructed a cloud product functionality and performance KG using the dependency information of layers and entities to represent complicated pre-post logical connections. The KG was designed according to the cloud service model. Second, we designed an improved PageRank algorithm to obtain the importance weight for each functionality and performance, which replaces the original average method with the proportion of connection weight. We considered the release time of the functionality, launch time of the product, and last update time of the product as crucial factors in the recommendation score to reflect the importance of the functionality and current development stage of the product. Finally, our method recommended a product set based on the weighted scores from the above results. In addition, we constructed a cloud product functionality dataset containing 339 functionalities. The experimental results show that the proposed method can generate a closely related set of products, leading to improved accuracy and higher satisfaction compared to mainstream methods.

Our objective is to survey recent results concerning the evolution of random networks and related extreme value statistics, which are a subject of interest due to numerous applications. Our survey concerns the statistical methodology but not the structure of random networks. We focus on the problems arising in evolving networks mainly due to the heavy-tailed nature of node indices. Tail and extremal indices of the node influence characteristics like in-degrees, out-degrees, PageRanks, and Max-linear models arising in the evolving random networks are discussed. Related topics like preferential and clustering attachments, community detection, stationarity and dependence of graphs, information spreading, finding the most influential leading nodes and communities, and related methods are surveyed. This survey tries to propose possible solutions to unsolved problems, like testing the stationarity and dependence of random graphs using known results obtained for random sequences. We provide a discussion of unsolved or insufficiently developed problems like the distribution of triangle and circle counts in evolving networks, or the clustering attachment and the local dependence of the modularity, the impact of node or edge deletion at each step of evolution on extreme value statistics, among many others. Considering existing techniques of community detection, we pay attention to such related topics as coloring graphs and anomaly detection by machine learning algorithms based on extreme value theory. In order to understand how one can compute tail and extremal indices on random graphs, we provide a structured and comprehensive review of their estimators obtained for random sequences. Methods to calculate the PageRank and PageRank vector are shortly presented. This survey aims to provide a better understanding of the directions in which the study of random networks has been done and how extreme value analysis developed for random sequences can be applied to random networks. [ABSTRACT FROM AUTHOR]

Graph coloring is widely used to parallelize scientific applications by identifying subsets of independent tasks that can be executed simultaneously. Graph coloring assigns colors the vertices of a graph, such that no adjacent vertices have the same color. The number of colors used corresponds to the number of parallel steps in a real-world end-application. Therefore, the total runtime of the graph coloring kernel adds to the overall parallel overhead of the real-world end-application, whereas the number of the vertices of each color class determines the number of the independent concurrent tasks of each parallel step, thus affecting the amount of parallelism and hardware resource utilization in the execution of the real-world end-application. In this work, we propose a high-performance graph coloring algorithm, named ColorTM, that leverages Hardware Transactional Memory (HTM) to detect coloring inconsistencies between adjacent vertices. ColorTM detects and resolves coloring inconsistencies between adjacent vertices with an eager approach to minimize data access costs, and implements a speculative synchronization scheme to minimize synchronization costs and increase parallelism. We extend our proposed algorithmic design to propose a balanced graph coloring algorithm, named BalColorTM, with which all color classes include almost the same number of vertices to achieve high parallelism and resource utilization in the execution of the real-world end-applications. We evaluate ColorTM and BalColorTM using a wide variety of large real-world graphs with diverse characteristics. ColorTM and BalColorTM improve performance by 12.98 × and 1.78 × on average using 56 parallel threads compared to prior state-of-the-art approaches. Moreover, we study the impact of our proposed graph coloring algorithmic designs on a popular end-application, i.e., Community Detection, and demonstrate the ColorTM and BalColorTM can provide high-performance improvements in real-world end-applications across various input data given. [ABSTRACT FROM AUTHOR]

Centrality is a relevant topic in the field of network research, due to its various theoretical and practical implications. In general, all centrality metrics aim at measuring the importance of nodes (according to some definition of importance), and such importance scores are used to rank the nodes in the network, therefore the rank improvement is a strictly related topic. In a given network, the rank improvement is achieved by establishing new links, therefore the question shifts to which and how many links should be collected to get a desired rank. This problem, also known as link-building has been shown to be NP-hard, and most heuristics developed failed in obtaining good performance with acceptable computational complexity. In this article, we present LB--GDM, a novel approach that leverages Geometric Deep Learning to tackle the link-building problem. To validate our proposal, 31 real-world networks were considered; tests show that LB--GDM performs significantly better than the state-of-the-art heuristics, while having a comparable or even lower computational complexity, which allows it to scale well even to large networks. [ABSTRACT FROM AUTHOR]

Computational drug repositioning approaches are important, as they cost less compared to the traditional drug development processes. This study proposes a novel network-based drug repositioning approach, which computes similarities between disease-causing genes and drug-affected genes in a network topology to suggest candidate drugs with highest similarity scores. This new method aims to identify better treatment options by integrating systems biology approaches. It uses a protein-protein interaction network that is the main topology to compute a similarity score between candidate drugs and disease-causing genes. The disease-causing genes were mapped on this network structure. Transcriptome profiles of drug candidates were taken from the LINCS project and mapped individually on the network structure. The similarity of these two networks was calculated by different network neighborhood metrics, including Adamic-Adar, PageRank and neighborhood scoring. The proposed approach identifies the best candidates by choosing the drugs with significant similarity scores. The method was experimented on melanoma, colorectal, and prostate cancers. Several candidate drugs were predicted by applying AUC values of 0.6 or higher. Some of the predictions were approved by clinical phase trials or other in-vivo studies found in literature. The proposed drug repositioning approach would suggest better treatment options with integration of functional information between genes and transcriptome level effects of drug perturbations and diseases.

Forensic linguistics and stylometry have in the exploration of linguistic patterns one of their fundamental tools. Mathematical structures such as complex multilayer networks and hypergraphs provide remarkable resources to represent and analyze texts. In this paper, we present a model that includes some specific mesoscopic relations between the different types of words in a corpus (lexical words, verbs, linking words, other words) according to the sentences or paragraphs in which they appear. This model is supported by various mathematical structures such as partial multiline graphs, multilayer hypergraphs, and their derivative graphs. The methodology proposed from this new point of view is of singular help to find meaningful sentences from any text to set up an automatic summary of the text and, eventually, to determine its linguistic level. [ABSTRACT FROM AUTHOR]

In this paper we propose several new quantum computation algorithms as an original contribution to the domain of PageRank algorithm theory, Spectral Graph Theory and Quantum Signal Processing. We first propose an application to PageRank of the HHL quantum algorithm for linear equation systems. We then introduce one of the first Quantum-Based Algorithms to perform a directed Graph Fourier Transform with a low gate complexity. After proposing a generalized PageRank formulation, based on ideas stemming from Spectral Graph Theory, we show how our quantum directed graph Fourier Transform can be applied to compute our generalized version of the PageRank. [ABSTRACT FROM AUTHOR]

Network topology is the basic for the development of traffic management and control. In a road network, bayonets with installation of surveillance facilities are key components to recognize traffic congestion from time to time. Therefore, identifying the essential bayonets in a road network becomes one of the most efficient ways to alleviate traffic congestion for traffic engineers and transport department. To do so, this paper aims to propose a novel sorting algorithm based on similarity measurements and traffic flow information to efficiently identify key bayonets in road networks. Our research results show that by analyzing the bayonet data in a fixed period of time in a medium-sized city of China, we have successfully identified the location of key bayonet points. Most of these key bayonet points are closed to residential areas and important traffic stations. The rank of these bayonet points can help the city managers better understand the topological characteristics of the road network as well as the propagation of congestion so as to make the traffic policies or control strategies for traffic congestion alleviation. [ABSTRACT FROM AUTHOR]

With the development of avionics industry, it is difficult for traditional combat equipment node evaluation method to meet our requirements under complex combat system. This paper presents a method of node importance evaluation which is suitable for modern avionics field and can be used for reference in other combat fields. In order to make better use of the different features of the node itself and the different connections between nodes, we use TOPSIS algorithm to model the characteristics of the node itself, and PageRank to measure the interdependence of all nodes. Therefore, a novel node contribution evaluation algorithm based on TOPSIS and PageRank is proposed in this paper. In addition, after the evaluation of node contribution, we found that there was also a functional relationship between the operational information entropy in the whole graph and the contribution of these nodes. On this basis, information entropy evaluation algorithm of the overall combat map is further proposed. After a lot of experiments, the reliability of our algorithm is evaluated on the indexes of the node's destruction-resistant performance and information transfer efficiency. Compared with the traditional universal algorithm, our proposed algorithm shows more interpretable and robust results in the field of avionics. [ABSTRACT FROM AUTHOR]

The multi-step matrix splitting iteration (MPIO) for computing PageRank is an efficient iterative method by combining the multi-step power method with the inner-outer iterative method. In this paper, with the aim of accelerating the computation of PageRank problems, a new method is proposed by preconditioning the MPIO method with an adaptive generalized Arnoldi (GArnoldi) method. The new method is called as an adaptive GArnoldi-MPIO method, whose construction and convergence analysis are discussed in detail. Numerical experiments on several PageRank problems are reported to illustrate the effectiveness of our proposed method. [ABSTRACT FROM AUTHOR]

Uncertainty has always been a threat to system performance in both manufacturing and service industries. Although cost-budgeting may limit available resources, a more flexible structure can still improve the system's ability to deal with uncertainty. In this study, we develop a new measure to help find a more flexible structure without extensive simulation. We create a PageRank-analogous score whereby we can calculate the Flexibility Gap (FG) index to predict the better of two alternative structures with topological information only. We theoretically analyze how the FG index recognizes flexible sparse structures such as expander graphs with high expansion ratios. Numerical experiments show that the FG index is effective in ranking the flexibility performance of different structures in terms of average waiting time and expected lost sales. Moreover, we extend the FG index with minimal modification to accommodate the case of imperfect flexibility (i.e., flexible suppliers with shrinking capacities) and demonstrate that the generalized FG index is still a good predictor of expected lost sales. Our approach provides a novel view to explain flexibility. That is, sparse structures with higher graph expansion ratios disperse the demand fluctuation more "rapidly" among resources to cushion the shock of uncertainty. [ABSTRACT FROM AUTHOR]

Today financial institutions have been investing billions of US dollars to detect money laundering. When financial institutions are found to have their customers conduct money laundering through them, they are subjected to large penalties. Moreover, their reputation suffers greatly through public exposure. In response, financial institutions have been exploring opportunities to use graph machine learning algorithms. This paper describes one of those algorithms called Anti-TrustRank and demonstrates how it can be used to identify money launderers. In contrast to many other algorithms, Anti-TrustRank calls for selecting a very small set of customers to be confirmed by human experts (e.g., compliance officers or analysts) as money launderers. Once this set has been identified, Anti-TrustRank seeks out customers linked (either directly or indirectly) to those money launderers. [ABSTRACT FROM AUTHOR]

We consider a network-based framework for studying causal relationships in financial markets and demonstrate this approach by applying it to the entire U.S. stock market. Directed networks (referred to as “causal market graphs”) are constructed based on publicly available stock prices time series data during 2001–2020, using Granger causality as a measure of pairwise causal relationships between all stocks. We consider the dynamics of structural properties of the constructed network snapshots, group stocks into network-based clusters, as well as identify the most “influential” market sectors via the PageRank algorithm. Interestingly, we observed drastic changes in the considered network characteristics in the years that corresponded to significant global-scale events, most notably, the financial crisis of 2008 and the COVID-19 pandemic of 2020.

This paper proposes a method to evaluate academic impact that focuses on spatial context in which citations occur in sections of citing papers. Previous studies measured impact of papers using external factors such as journals, time, and authors. However, these methods overlooks context of citations, leading to problem of treating papers with same citation counts equivalently. To overcome this issue, we designed a citation network by reflecting on the spatial context in which cited papers are cited in the citing paper and measured their impact. Spatial context is defined by the specific section of the citing paper (Introduction, Method, Result, Discussion, Conclusion) where the citation appears. We collected 818 citing papers and 13,257 cited papers from 2013–2022 from Journal of Informetrics and constructed a context-reflected citation network. Further, we utilized CRITIC method and weighted PageRank algorithm for measuring section-specific weights and impact. Results obtained in this study suggest that the impact of cited papers varies significantly depending on the section context in which they appear. We use Kendall τ coefficient for analyzing correlation between "times cited" rankings and contextual PageRank. The Kendall τ coefficient between two ranks for entire dataset is 0.473. This study provides a multidimensional framework to assess the impact of academic papers, suggesting that future evaluations should consider not only the number of citations but also their context. • The proposed method can focus on the citation context in academic impact. • CRITIC method and weighted PageRank algorithm apply for impact assessment. • Real data from Journal of Informetrics has been employed for the citation network. • Results indicate impact variation in different paper's spatial context. • Kendall τ coefficient revealed correlation in citations and rankings. [ABSTRACT FROM AUTHOR]

Creating policy measures is the final step in the process of e-learning roadmap development. Policy measures can be seen as long-term activities that need to be implemented and constantly upgraded to achieve strategic goals. For resource allocation, it is useful to prioritize policy measures. Prioritization can be implemented using multi-criteria decision-making methods. This paper analyzes policy measures in the Maldives National University’s e-learning roadmap using the social network analysis process (SNAP), which includes the analytic hierarchy process (AHP), the decision-making trial and evaluation laboratory (DEMATEL), and the PageRank centrality. In policy measure evaluation, there were more than 20 participants: persons with managerial functions at the Maldives National University (MNU) (deans, heads of departments) and persons in lecturer and researcher positions. By using the AHP, participants prioritized policy measures with respect to their importance to them. By using the DEMATEL, participants identified and prioritized policy measures with respect to their effect on other measures. Finally, by using the SNAP, it was possible to determine the prioritization list for resource allocation since it aggregates the aspects of the policy measures, their importance, and their effect on other measures.