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Many deep learning applications deployed in dynamic environments change over time, in which the training models are supposed to be continuously updated with streaming data to guarantee better descriptions of data trends. However, most state-of-the-art learning frameworks support well in offline training methods while omitting online model updating strategies. In this work, we propose and implement iDlaLayer , a thin middleware layer on top of existing training frameworks that streamlines the support and implementation of online deep learning applications. In pursuit of good model quality and fast data incorporation, we design a Data Life Aware model updating strategy (DLA), which builds training data samples according to contributions of data from different life stages, and considers the training cost consumed in model updating. We evaluate iDlaLayer's performance through simulations and experiments based on TensorflowOnSpark with three representative online learning workloads. Our experimental results demonstrate that iDlaLayer reduces the overall elapsed time of ResNet, DeepFM and PageRank by 11.3, 28.2, and 15.2 percent compared to the periodic update strategy, respectively. It further achieves an average 20 percent decrease in training cost and brings about a 5 percent improvement in model quality against the traditional continuous training method.

Ranking on graphs is a centerpiece in many high-impact application domains, such as information retrieval, recommender systems, team management, neuroscience and many more. PageRank, along with many of its variants, is widely used across these application domains thanks to its mathematical elegance and the superior performance. Although PageRank and its variants are effective in ranking nodes on graphs, they often lack an efficient and effective way to audit the ranking results in terms of the input graph structure, e.g., which node or edge in the graph contributes most to the top-1 ranked node; which subgraph plays a crucial role in generating the overall ranking result? In this paper, we propose to audit graph ranking by finding the influential graph elements (e.g., edges, nodes, attributes, and subgraphs) regarding their impact on the ranking results. First, we formulate graph ranking auditing problem as quantifying the influence of graph elements on the ranking results. Second, we show that our formulation can be applied to a variety of graph structures. Third, we propose effective and efficient algorithms to find the top- $k$ k influential edges/nodes/subgraph. Finally, we perform extensive empirical evaluations on real-world datasets to demonstrate that the proposed methods ( Aurora ) provide intuitive auditing results with linear scalability.

International audience; In this study, the Finite-Element Model Updating (FEMU) technique is used to identify hyperelastic parameters from only one heterogeneous test. A residual considering measured and identified stretches as well as the global reaction force of the specimen is built. The originality of this paper is to investigate the feasibility of the resolution of this minimisation problem using the Inverse-PageRank-particle swarm optimisation (PSO) for identifying hyperelastic parameters. For that purpose, the so-called PSO technique has been enriched with a PageRank algorithm to adapt iteratively the PSO parameters. As the paper examines whether Inverse-PageRank-PSO is adapted or not to the minimisation of the objective function in the present case, only two basic hyperelastic models have been considered.

With the rapid development of network technology, computer viruses have developed at a fast pace. The threat of computer viruses persists because of the constant demand for computers and networks. When a computer virus infects a facility, the virus seeks to invade other facilities in the network by exploiting the convenience of the network protocol and the high connectivity of the network. Hence, there is an increasing need for accurate calculation of the probability of computer-virus-infected areas for developing corresponding strategies, for example, based on the possible virus-infected areas, to interrupt the relevant connections between the uninfected and infected computers in time. The spread of the computer virus forms a scale-free network whose node degree follows the power rule. A novel algorithm based on the binary-addition tree algorithm (BAT) is proposed to effectively predict the spread of computer viruses. The proposed BAT utilizes the probability derived from PageRank from the scale-free network together with the consideration of state vectors with both the temporal and learning effects. The performance of the proposed algorithm was verified via numerous experiments.

OSMnx is a Python package for downloading OpenStreetMap street network data and then constructing it into NetworkX graphs. OSMnx can simplify and correct the network’s topology automatically to ensure that nodes actually exclusively represent intersections and dead-ends. Once the network is constructed and corrected, OSMnx can calculate shortest paths from one node to another. It can also calculate various network measures relevant to urban design and transportation as well as statistical physics, including intersection density, average intersection degree, edge density, average street segment length, circuity, clustering coefficients, betweenness centrality, closeness centrality, PageRank, and many more. Its built-in visualization capabilities leverage matplotlib to easily plot routes, one-way streets, dead-ends, high/low connectivity intersections, and figure-ground diagrams of street networks and urban form.

Motivated by the increasing need to understand the distributed algorithmic foundations of large-scale graph computations, we study some fundamental graph problems in a message-passing model for distributed computing where k ≥ 2 machines jointly perform computations on graphs with n nodes (typically, n >> k). The input graph is assumed to be initially randomly partitioned among the k machines, a common implementation in many real-world systems. Communication is point-to-point, and the goal is to minimize the number of communication rounds of the computation. Our main contribution is the General Lower Bound Theorem , a theorem that can be used to show non-trivial lower bounds on the round complexity of distributed large-scale data computations. This result is established via an information-theoretic approach that relates the round complexity to the minimal amount of information required by machines to solve the problem. Our approach is generic, and this theorem can be used in a “cookbook” fashion to show distributed lower bounds for several problems, including non-graph problems. We present two applications by showing (almost) tight lower bounds on the round complexity of two fundamental graph problems, namely, PageRank computation and triangle enumeration . These applications show that our approach can yield lower bounds for problems where the application of communication complexity techniques seems not obvious or gives weak bounds, including and especially under a stochastic partition of the input. We then present distributed algorithms for PageRank and triangle enumeration with a round complexity that (almost) matches the respective lower bounds; these algorithms exhibit a round complexity that scales superlinearly in k , improving significantly over previous results [Klauck et al., SODA 2015]. Specifically, we show the following results: PageRank: We show a lower bound of Ὼ(n/k 2 ) rounds and present a distributed algorithm that computes an approximation of the PageRank of all the nodes of a graph in Õ(n/k 2 ) rounds. Triangle enumeration: We show that there exist graphs with m edges where any distributed algorithm requires Ὼ(m/k 5/3 ) rounds. This result also implies the first non-trivial lower bound of Ὼ(n 1/3 ) rounds for the congested clique model, which is tight up to logarithmic factors. We then present a distributed algorithm that enumerates all the triangles of a graph in Õ(m/k 5/3 + n/k 4/3 ) rounds.

Manual debugging is notoriously tedious and time-consuming. Therefore, various automated fault localization techniques have been proposed to help with manual debugging. Among the existing fault localization techniques, spectrum-based fault localization (SBFL) is one of the most widely studied techniques due to being lightweight. The focus of the existing SBFL techniques is to consider how to differentiate program entities (i.e., one dimension in program spectra); indeed, this focus is aligned with the ultimate goal of finding the faulty lines of code. Our key insight is to enhance the existing SBFL techniques by additionally considering how to differentiate tests (i.e., the other dimension in program spectra), which, to the best of our knowledge, has not been studied in prior work. We present our basic approach, PRFL, a lightweight technique that boosts SBFL by differentiating tests using PageRank algorithm. Specifically, given the original program spectrum information, PRFL uses PageRank to recompute the spectrum by considering the contributions of different tests. Next, traditional SBFL techniques are applied on the recomputed spectrum to achieve more effective fault localization. On top of PRFL, we explore PRFL+ and PRFL $_{\mathrm{MA}}$ MA , two variants which extend PRFL by optimizing its components and integrating Method-level Aggregation technique, respectively. Though being simple and lightweight, PRFL has been demonstrated to outperform state-of-the-art SBFL techniques significantly (e.g., ranking 39.2% / 82.3% more real/artificial faults at Top-1 compared with the most effective traditional SBFL technique) with low overhead (e.g., around 6 minutes average extra overhead on real faults) on 395 real faults from 6 Defects4J projects and 96925 artificial (i.e., mutation) faults from 240 GitHub projects. To further validate PRFL's effectiveness, we compare PRFL with multiple recent proposed fault localization techniques (e.g., Multric, Metallaxis and MBFL-hybrid-avg), and the experimental results show that PRFL outperforms them as well. Furthermore, we study the performance of PRFL $_{\mathrm{MA}}$ MA , and the experimental results present it can locate 137 real faults (73.4% / 24.5% more compared with the most effective SBFL/PRFL technique) and 35058 artificial faults (159.6% / 28.1% more than SBFL/PRFL technique) at Top-1. At last, we study the generalizability of PRFL on another benchmark, Bugs.jar, and the result shows PRFL can help locate around 30 percent more faults at Top 1.

Ranking firms in an inter-firm transaction network is a crucial task for identifying key players in an industry, thereby explaining the agglomeration of economic activities and assisting with competitor identification. To the best of our knowledge, despite the advantages of network-based approaches in market analysis, few studies have employed network analysis tools to rank firms. However, these studies failed to capture the characteristics of inter-firm transaction networks (i.e., evolving over time, having multiple edges between nodes, among others). In this study, we propose a new ranking method, FirmRank, that identifies key players based on centrality metrics in network analysis, leveraging inter-firm transactions to discern the characteristics of an inter-firm transaction network. Our proposed ranking method is evaluated using real-world datasets from a corporate information database, and the evaluation results demonstrate the superiority of our method over well-known ranking methods—PageRank and age-based PageRank.

Internet connectivity is now considered to be a basic human right and in this era of information technology, more efficient ways of browsing the web are being found. The most popular search engine, Google uses its ‘Page Rank Algorithm’ for its searches. In this paper, we aim to find out a more efficient way of searching the web and better organising the search result based on the categories of the pages. The results of the proposed algorithm are compared with the existing Page Rank algorithms, which include the original PageRank Algorithm and a Weighted PageRank Algorithm. The results obtained in this paper have been experimentally compared with the data of the above-mentioned algorithms using comparison graphs.

Background The Internet is increasingly used as a source of information. This study investigates with a multidimensional methodology the quality of information of websites dedicated to obesity treatment and weight-loss interventions. We compared websites in English, a language that it is used for the international scientific divulgation, and in Italian, a popular local language. Methods Level of Evidence: Level I, systematic review search on four largely used search engines. Duplicated and unrelated websites were excluded. We checked: popularity with PageRank; technological quality with Nibbler; readability with the Flesch Reading Ease test or the Gulpease readability index; quality of information with the DISCERN scale, the JAMA benchmark criteria, and the adherence to the Health on the Net Code. Results 63 Italian websites and 41 English websites were evaluated. English websites invested more in the technological quality especially for the marketing, experience of the user, and mobile accessibility. Both the Italian and English websites were of poor quality and readability. Conclusions These results can inform guidelines for the improvement of health information and help Internet users to achieve a higher level of information. Users must find benefits of treatment, support to the shared decision-making, the sources used, the medical editor's supervision, and the risk of postponing the treatment.

Text summarization presents several challenges such as considering semantic relationships among words, dealing with redundancy and information diversity issues. Seeking to overcome these problems, we propose in this paper a new graph-based Arabic summarization system that combines statistical and semantic analysis. The proposed approach utilizes ontology hierarchical structure and relations to provide a more accurate similarity measurement between terms in order to improve the quality of the summary. The proposed method is based on a two-dimensional graph model that makes uses statistical and semantic similarities. The statistical similarity is based on the content overlap between two sentences, while the semantic similarity is computed using the semantic information extracted from a lexical database whose use enables our system to apply reasoning by measuring semantic distance between real human concepts. The weighted ranking algorithm PageRank is performed on the graph to produce significant score for all document sentences. The score of each sentence is performed by adding other statistical features. In addition, we address redundancy and information diversity issues by using an adapted version of Maximal Marginal Relevance method. Experimental results on EASC and our own datasets showed the effectiveness of our proposed approach over existing summarization systems.

It is of great significance to predict the results accurately based on the statistics of sports competition for participants research, commercial cooperation, advertising, and gambling profit. Aiming at the phenomenon that the PageRank page sorting algorithm is prone to subject deviation, the category similarity between pages is introduced into the PageRank algorithm. In the PR value calculation formula of the PageRank algorithm, the factor W(u, v) between pages is added to replace the original Nu (the number of links to page u). In this way, the content category between pages is considered, and the shortcoming of theme deviation will be improved. The time feedback factor in the PageRank-time algorithm is used for reference, and the time feedback factor is added to the first improved PR value calculation formula. Based on statistics from 1230 games during the NBA 2018-2019 regular season, this paper ranks the team strength with improved PageRank algorithm and compares the results with the ranking of regular-season points and the result of playoffs. The results show that it is consistent with the regular-season points ranking in the eastern division by the use of improved PageRank algorithm, but there is a difference in the second ranking in the western division. In the prediction of top four in playoffs, it predicts three of the four teams.

Finding influential users in online social networks (OSNs) is an important problem with many possible useful applications. Many methods have been proposed to identify influential users in OSNs. PageRank and HITs are two well known examples that determine influential users through link analysis. In recent years, new models that consider both content and social network links have been developed. The Hub and Authority Topic (HAT) model is one that extends HITS to identify topic-specific hubs and authorities by jointly learning hubs, authorities, and topical interests from users’ relationship and textual content. However, many of the previous works are confined to identifying influential users within a single OSN. These models, when applied to multiple OSNs, could not learn influential users under a common set of topics nor address platform preferences. In this paper, we therefore propose the MPHAT model, an extension of HAT, to jointly model the topic-specific hub users, authority users, their topical interests and platform preferences. We evaluate MPHAT against several existing state-of-the-art methods in three tasks: (i) modeling of topics, (ii) platform choice prediction, and (iii) link recommendation. Based on our extensive experiments in multiple OSNs settings using synthetic datasets and real-world datasets from Twitter and Instagram, we show that MPHAT is comparable to state-of-the-art topic models in learning topics but outperforms the state-of-the-art models in platform prediction and link recommendation tasks. We also empirically demonstrate the ability of MPHAT to determine influential users within and across multiple OSNs.

The air transportation industry is one big network that is expected to grow continuously, and Asia-Pacific is the fastest growing region as of 2018 data. We apply weighted network analysis to explore the key network characteristics of the Asian international passenger aviation market in 2014 and 2018. For the analysis, networks are constructed based on international passengers' origin-destination pair data from 28 Asian airlines in 2014 and 2018. Previously published social network analysis (SNA) on the air transportation network largely used unweighted centrality metrics, whereas the actual situation is a weighted network. We implement Mean Associations (MA), Triangle Betweenness Centrality (TBC), and Weighted PageRank (WPR), as new weighted centrality metrics, to effectively reflect actual passenger traffic among airports beyond simple connections. Our weighted network analysis indicates that SIN, ICN, HKG, NRT, MNL, BKK, TPE, HND, PEK, and KUL are the top-ranked hub airports, followed by SGN, PVG, CAN, DEL, SYD, KIX and CGK, and that the low-cost carriers have almost fully developed a ‘hub-and-spoke’ structure over the 4-year study period and are increasing their impact on the centrality of airports in the region.

This work presents FuzzyFeatureRank, a new feature reduction method inspired on PageRank to reduce the dimensionality of the feature space in supervised classification problems. More precisely, as it relies on a weighted directed graph, it is ultimately inspired on TextRank, a PageRank based method that adds weights to the edges to express the strength of the connections between nodes. The method is based on dividing each original feature used to describe the data into a set of fuzzy predicates and then ranking all of them by their ability to differentiate among classes in the light of the training set. In order to do that, both the information gained by each predicate and their redundancy with other already selected predicates are taken into account. The fuzzy predicates with the best scores can then be used as a reduced input to construct fuzzy classifiers that consider only the preselected predicates to build the antecedents of the fuzzy rules. The novelty of the proposal relies on being an approach halfway between feature selection and feature extraction approaches, being able to improve the discrimination ability of the original features but preserving the interpretability of the new features in the sense that they are fuzzy expressions. The experimental results support the suitability of the proposal.

Community detection in networks is valuable in analyzing, designing, and optimizing complex network. Recently, the nonnegative matrix factorization (NMF) method has successfully uncovered the community structure in the complex networks. However, most of community detection methods based on NMF require the number of the community as a prior information. To address this problem, in this paper, we use the improved density peak clustering (DPC) to obtain the number of centers as the pre-assigned parameter for nonnegative matrix factorization. The proposed algorithm first calculates the modified PageRank of nodes as the density indexes and then draws a decision graph to obtain the hubs of the network. By means of Markov chain model of a random walk, we execute NMF on one expansion feature matrix. Finally, we compare and analyze the performance of different algorithms on artificial networks and real-world networks. Experimental results indicate that the proposed method is superior to the state-of-the-art methods.

This paper presents four centrality measurements applied to an alternating current (AC) microgrid (MG) modeled as a multiplex network. The MG secondary control is separated into a frequency and a power-sharing layers, each one with a different adjacency matrix. A physical layer is also considered with an admittance matrix representing the impedances among the inverters. Centrality measures are used to determine the importance of nodes in separate layers, thereafter adjacency and Laplacian matrices are redefined to calculate the role of nodes in the multiplex system. First, a global adjacency matrix is calculated by the matrix sum of each adjacency matrix. Second, the adjacency matrix is calculated by a supra-Laplacian matrix. The first eigenvalue of the perturbed matrix is used to determine the diffusivity in the network using as leaders the sets obtained by the centrality measures. The role of the nodes in the system is verified in a simulated MG model of 37 nodes. Degree centrality and energy Laplacian measures present similar sets of nodes; however, the fastest set of nodes is found using the Eigenvector measurements for uniform and supra Laplacian approach.

Local community detection consists of finding a group of nodes closely related to the seeds, a small set of nodes of interest. Such groups of nodes are densely connected or have a high probability of being connected internally than their connections to other clusters in the network. Existing local community detection methods focus on finding either one local community that all seeds are most likely to be in or finding a single community for each of the seeds. However, a seed member usually belongs to multiple local overlapping communities. In this work, we present a novel method of detecting multiple local communities to which a single-seed member belongs. The proposed method consists of three key steps: 1) local sampling with Personalized PageRank (PPR); 2) using the sparseness generated by a sparse nonnegative matrix factorization (SNMF) to estimate the number of communities in the sampled subgraph; and 3) using SNMF soft community membership vectors to assign nodes to communities. The proposed method shows favorable accuracy performance and a good conductance compared with state-of-the-art community detection methods by experiments using a combination of artificial and real-world networks.

Personalized PageRank (PPR) is a classic measure of the relevance among different nodes in a graph, and has been applied in numerous systems, such as Twitter's Who-To-Follow and Pinterest's Related Pins. Existing work on PPR has mainly focused on three general types of queries, namely, single-pair PPR, single-source PPR, and all-pair PPR. However, we observe that there are applications that rely on a new query type (referred to as batch one-hop PPR ), which takes as input a set $S$ S of source nodes and, for each node $s \in S$ s ∈ S and each of $s$ s 's neighbor $v$ v , asks for the PPR value of $v$ v with respect to $s$ s . None of the existing PPR algorithms is able to efficiently process batch one-hop queries, due to the inherent differences between batch one-hop PPR and the three general query types. To address the limitations of existing algorithms, this paper presents Baton , an algorithm for batch one-hop PPR that offers both strong theoretical guarantees and practical efficiency. Baton leverages the characteristics of one-hop PPR to avoid unnecessary computation, and it incorporates advanced mechanisms to improve the cost-effectiveness of PPR derivations. Extensive experiments on benchmark datasets show that Baton is up to three orders of magnitude faster than the state of the art, while offering the same accuracy.

Background: The hydrographic network of the basins of the department of Quindio, located in Colombia, South America, suffers from pollution due to industries such as tanneries, mining, food processing, among others. Goals: Represent a hydrographic network of a basin employing a graph and study its most representative measures of centrality; as a case study, we considered the hydrologic network of the basins of the department of Quindio. Methods: The theory of networks and the analysis of centrality measures such as degree distribution, PageRank, and eccentricity; also, topological measures such as density and assortativity. Results: The study provided a different approach to research on the properties of river networks. The grade distribution shows a network with input grades of one, two, and three, while the output grade is only one or zero, consistent with a typical mountain basin. Measurements such as density and assortativity showed a low-density network with no defined connection patterns. The centrality of PageRank and eccentricity highlighted the leading causes and the most important rivers according to the way the hydrographic network is connected. Conclusions: The study concluded that by using network theory, we obtained a global vision of the network, understanding its structure and allowing us to visualize which areas of the network are vulnerable. PageRank’s centrality allowed us to identify well-monitored nodes in the network and those that need care; this same distribution highlighted zones in the network that are more prone to contamination. Finally, the eccentricity determined the center of the network (sector of “La Maria”) as one of the network’s most vulnerable areas.

The identification of influential nodes in complex networks is an open issue. To address it, many centrality measures have been proposed, among which the most representative iteration algorithm is ...

The existence of botnet puts people in an extremely insecure environment, which has seriously affected the development of Internet of Things (IoT). In order to prevent the formation of a botnet, it is necessary to understand the propagation behaviors and influence factors. As IoT devices become more intelligent, they can gradually generate social characteristics by mimicking human behaviors. However, in the process of coping with the botnet problem, little consideration has been given to the potential social characteristics of IoT. In this article, we build a dynamic botnet propagation model (i.e., IoT-BSI model) to study the influences of two social characteristics (i.e., device’s spread capability and device’s identification ability) on botnet formation. First, in terms of device’s spread capability, this article makes great improvements on the K- shell decomposition algorithm and calculates this characteristic more accurately. Second, this article divides the device’s identification ability into the rational identification ability and the irrational identification ability based on the sociological theory, and the calculation of the former is mainly realized by utilizing the PageRank idea. Third, this article applies the mean-field equation theory to analyze the dynamic characteristics of botnet propagation theoretically. Finally, the comprehensive results show that our model is not merely more consistent with the actual situation but also performs better than four compared models.

We tackle a major challenge of information filtering on social media (SM): rather than address the general question “what are people talking about on SM?”, we consider a finer question, “what are ... talking about on SM?”, where ... stands for some subpopulation of SM users of interest. We take a set expansion approach, where a seed of example members of the target subpopulation is initially defined, and additional SM users who belong to that subpopulation are identified, thus enabling the effective tracking of relevant information that pertains to that subpopulation on SM. Specifically, the Personalized PageRank (PPR) random walk measure is iteratively applied to detect additional members of the subpopulation based on their structural similarity to the seed set within the social media graph. There are several main contributions of this work. We outline Splash PPR, an efficient distributed computation of PPR adapted for potentially large seed sets and very large SM graphs. Using Splash PPR, we examine and tune graph representations towards the retrieval of two subpopulations from Twitter, namely human rights Activists, and Machine Learning practitioners. We believe this work is first to introduce and evaluate a generic framework for subpopulation identification at scale.

Purpose The number of citations has been widely used to measure the significance of a paper. However, there is a need in introducing another index to determine superiority or inferiority of papers with the same number of citations. We determine superiority or inferiority of papers by using the ranking based on the number of citations and PageRank. Design/methodology/approach We show the positive linear correlation between Citation Rank (the ranking of the number of citation) and PageRank. On this basis, we identify high-quality, prestige, emerging, and popular papers. Findings We found that the high-quality papers belong to the subjects of biochemistry and molecular biology, chemistry, and multidisciplinary sciences. The prestige papers correspond to the subjects of computer science, engineering, and information science. The emerging papers are related to biochemistry and molecular biology, as well as those published in the journal “Cell.” The popular papers belong to the subject of multidisciplinary sciences. Research limitations We analyze the Science Citation Index Expanded (SCIE) from 1981 to 2015 to calculate Citation Rank and PageRank within a citation network consisting of 34,666,719 papers and 591,321,826 citations. Practical implications Our method is applicable to forecast emerging fields of research subjects in science and helps policymakers to consider science policy. Originality/value We calculated PageRank for a giant citation network which is extremely larger than the citation networks investigated by previous researchers.

Online social networks, such as Facebook, Twitter, and Wechat have become major social tools. The users can not only keep in touch with family and friends, but also send and share the instant information. However, in some practical scenarios, we need to take effective measures to control the negative information spreading, e.g., rumors spread over the networks. In this paper, we first propose the minimizing influence of rumors (MIR) problem, i.e., selecting a blocker set $\mathcal {B}$ with $k$ nodes such that the users’ total activation probability by rumor source set $S$ is minimized. Then, we employ the classical independent cascade (IC) model as an information diffusion model. Based on the IC model, we prove that the objective function is monotone decreasing and non-submodular. To address the MIR problem effectively, we propose a two-stages method generating candidate set and selecting blockers for the general networks. Furthermore, we also study the MIR problem on the tree network and propose a dynamic programming guaranteeing the optimal solution. Finally, we evaluate proposed algorithms by simulations on synthetic and real-life social networks, respectively. Experimental results show our algorithms are superior to the comparative heuristic approaches, such as out-degree, betweenness centrality, and PageRank.

This paper proposed an approach for Arabic text summarization. Text summarization is one of the natural language processing's applications which is used for reducing the original text amount and retrieving only the important information from the original text. The Arabic language has a complex morphological structure which makes it very difficult to extract nouns to be used as a feature for summarization process. Therefore, Al-Khalil morphological analyzer is used to solve the problem of nouns extraction. The proposed approach is a graph-based system, which represents the document as a graph where the vertices of the graph are the sentences. A Modified PageRank algorithm is applied with an initial score for each node that is the number of nouns in this sentence. More nouns in the sentence mean more information, so nouns count used here as initial rank for the sentence. Edges between sentences are the cosine similarity between the sentences, to get a final summary that contains sentences with more information and well connected with each other. The process of text summarization consists of three major stages: pre-processing stage, features extraction and graph construction stage, and finally applying the Modified PageRank algorithm and summary extraction. The Modified PageRank algorithm used a different number of iterations to find the number returns the best summary results, and the extracted summary depends on compression ratio, taking into account removing redundancy depending on the overlapping between the sentences. To evaluate the performance of this approach EASC Corpus is used as a standard. LexRank and TextRank algorithms were used under the same circumstances, the proposed approach provides better results when compared with other Arabic text summarization techniques. The proposed approach performs efficiently with the number of iteration 10,000.

The purpose of this article is to present the new ISOWQ Rank ranking algorithm for the technical assessment of website quality. For evaluation purposes, the algorithm takes into account the IT technologies used on a website, compliance of the source code with the applicable standards and the structure of the text content. The paper also includes the results of comparative ranking algorithms.

Keywords are considered to be important words in the text and can provide a concise representation of the text. With the surge of unlabeled short text on the Internet, automatic keyword extraction task has proven useful in other information processing applications. Graph-based approaches are prevalent unsupervised models for this task. However, most of these methods emphasize the importance of the relation between words without considering other importance factors. Furthermore, when measuring the importance of a word in a text, the damping factor is set to 0.85 following PageRank. To the best of our knowledge, there is no existing work investigating the impact of the damping factor on the keyword extraction task. In addition, there are few publicly available labeled Chinese short text datasets for this task. In this article, we investigate the importance parts of words in a given document and propose an improved graph-based method for keyword extraction from short documents. Moreover, we analyze the impact of importance factors on performance. We also provide annotated long and short Chinese datasets for this task. The model is performed on Chinese and English datasets, and results show that our model obtains improvements in performance over the previous unsupervised models on short documents. Comparative experiments show that the damping factor is related to the text length, which is neglected in traditional methods.

Given a scientific collaboration network, how can we find a group of collaborators with high research indicator (e.g., h-index) and diverse research interests? Given a social network, how can we identify the communities that have high influence (e.g., PageRank) and also have similar interests to a specified user? In such settings, the network can be modeled as a multi-valued network where each node has d ( $$d \ge 1$$ ) numerical attributes (i.e., h-index, diversity, PageRank, similarity score, etc.). In the multi-valued network, we want to find communities that are not dominated by the other communities in terms of d numerical attributes. Most existing community search algorithms either completely ignore the numerical attributes or only consider one numerical attribute of the nodes. To capture d numerical attributes, we propose a novel community model, called skyline community, based on the concepts of k-core and skyline. A skyline community is a maximal connected k-core that cannot be dominated by the other connected k-cores in the d-dimensional attribute space. We develop an elegant space-partition algorithm to efficiently compute the skyline communities. Two striking advantages of our algorithm are that (1) its time complexity relies mainly on the size of the answer s (i.e., the number of skyline communities), and thus, it is very efficient if s is small; and (2) it can progressively output the skyline communities, which is very useful for applications that only require part of the skyline communities. In addition, we also develop three efficient graph reduction techniques to further speed up the proposed algorithms. Extensive experiments on both synthetic and real-world networks demonstrate the efficiency, scalability, and effectiveness of the proposed algorithm.

PurposeThe purpose of this paper is to evaluate Chinese indirect trade relations in the global trade network to observe if the objectives identified by Cai (2017) in the Belt and Road Initiative (BRI) are being fulfilled, especially with Latin America and the Caribbean (LAC) countries.Design/methodology/approachUsing data from the UNCTAD (2016) for the period 2011–2015, a normalized exports network is built. It is analyzed with the Forgotten Effects Theory and the PageRank algorithm. A Monte Carlo experiment with 10,000 replicates is performed to account for its volatility.FindingsThe paper identifies one instance in which China's peripheral countries are importing raw materials and commodities -–oil products – to produce low technological value-added products, which, in turn, are exported to China. LAC countries do not have significant indirect trade relations with China when the former is the origin country, while the latter is the destination in a trade relationship. The trade network has a clear core-periphery structure, with China belonging to its core, although being only the fourth most central node in the network.Originality/valueThis paper contributes with both a new methodology for the analysis of indirect trade relations and the results found for China under the BRI and its trade relationship with LAC economies.

Supervised keyword extraction methods usually require a large human-annotated corpus to train the model. Expensive manual labeling has made unsupervised technology using word graph networks attractive. Traditional word graph networks simply consider the co-occurrence relationship of words or the topological structure of the network, ignoring the influence of semantic relations between words on keyword extraction. To solve these problems, an unsupervised keyword extraction method based on word graph networks for both Chinese and English is proposed. This method uses word embedding to applying a “word attraction score” to semantic relevance between words in a document. Combination of the bias weight of the node and a weighted PageRank algorithm is used to compute the final scores of words. The experimental results demonstrate that the method is more effective than the traditional methods.

Unstructured big data processing requires efficient computational styles to rapidly analyze continuously changing data. Incremental processing is a promising technique to update search results without re-computing the whole process. One of the most important mining algorithms is PageRank which necessitates incremental processing and iterative computations to keep the mining results up-to-date. In this paper, a light and efficient PageRank algorithm is proposed using differential dataflow in the MapReduce programming style named DiffPageRank. The innovation of the proposed approach is investigated from two points of view: (1) the updating state is dependent on a partial order of changes in DiffPageRank computing, while the changes are applied based on complete order sequences in the standard incremental computing; and (2) in DiffPageRank, a set of updates rebuilds each version, while in the incremental systems, every single update continually bring up-to-date the current version state. DiffPageRank is compared with two other implementations of PageRank including standard and incremental PageRank in MapReduce in terms of processing time, CPU utilization and update speed of data mining results. The results show that if the changes on the input data are large, the efficiency of the differential method will considerably increase in terms of time than the standard and incremental PageRank approaches up to 61% and 86%, respectively. DiffPageRank is also compared with two state-of-the-art incremental graph processing benchmarks on Orkut and Twitter datasets in terms of execution time and the number of updates. The experimental comparisons on benchmark datasets show that our method reduces the number of updates and total execution time by using the differential MapReduce approach.

Nowadays, online social networks have become an essential part of humans. However, there are some dark side to this widespread use of online social networks. One of them is the fact that many attackers have succeeded to clone celebrities’ profiles and have attracted hundreds or thousands of followers. This type of forging has caused many problems for famous people. This phenomenon is commonly known as Identity Cloning Attack which is abbreviated to ICA in the literature. ICA occurs when a malicious user selects one of the famous users of a social network as his victim. The attacker then creates a user account similar to the victim’s profile and embarks on various malicious social activities. In this paper, we have proposed an automatic method to identify cloned profiles. This method consists of three main steps and is implemented on Hadoop framework using the MapReduce programming model. In the first step, we count the number of followers of each user and store it as an attribute for their profile. In the second step, the network users are clustered based on their profile attributes and their number of followers. Subsequently, we move all the profiles within the same cluster as the victim’s profile to the next step and consider them as suspicious profiles. The victim’s profile is a profile of a celebrity, where the proposed method is conducted to verify its authenticity. In the third step, we eventually select the profile with the highest rank as the valid profile. This method of ranking the profiles is based on the outcome of PageRank algorithm in the first step. This method is easily applicable and does not require any additional information for identifying the original user account. Furthermore, this method employs a distributed processing framework, limits the search space, and decreases the required computation by clustering the profiles. We have applied the suggested method to a dataset that we collected from Instagram. Our findings were quite promising, and in some situations, we were able to identify all the cloned profiles with a 100% accuracy. The results are comparable to the best ones in this area of study.

Online social networks (OSNs) have emerged as a major platform for sharing information through social relationships and are one of the major sources of big data. Social networks can even accommodate sharing of live streaming data among the connected users. However, social information on social networks is often locally exploited rather than capturing the changes in the entire network over time. Obtaining user’s influence statistics is limited only in their local vicinity, which may not facilitate capturing the changes in the user and post influences across the entire network, thereby resulting in lower accuracy while measuring user’s topical influence. Moreover, low-influence users always exist in the network publishing low-quality posts. With the objectives of accurately capturing highly influential users and posts, this article proposes a novel dynamic social sensing model, named dynamic PageRank (DPRank) model, to evaluate the dynamic topical influence of the users of social information on social networks during the social information evolution. We deploy our proposed model to real-world Twitter data sets, which demonstrates the effectiveness of our proposed model against notable existing methods while identifying the true influence of users and posts in a dynamically evolving social network.

As a typical keyword extraction technology, TextRank has been used in a wide variety of commercial applications, including text classification, information retrieval and clustering. In these applications, the parameters of TextRank, including the co-occurrence window size, iteration number and decay factor, are set roughly, which might affect the effectiveness of returned results. In this work, we conduct an empirical study on TextRank, towards finding optimal parameter settings for keyword extraction. The experiments are done in Hulth2003 and Krapivin2009 datasets, which are two real datasets. We first remove the stop word by an open published English stop word list XPO6. And then, we extract the word stems by Porter Stemmer. Porter Stemmer is a tool which can find the stems of words with multiple variants, discard redundant information, strengthen the filtering effect, and extract the effective features of the text fully. We carry out extensive experiments to evaluate the effects of the parameters to keywords extraction, and evaluate the effectiveness of corresponding results by Precision, Recall and Accuracy. Experimental results show that TextRank shows the best performance when setting co-occurrence window size $w =3$ , iteration number $t = 20$ , decay factor $c = 0.9$ and rank $k =10$ respectively, and the results are independent of the text length.

In the era of Internet of Things, cloud services are difficult to meet the real-time transmission requirements of users for the data generated in the edge of network especially for the Internet video services. Utilizing the devices at the edge of network, such as an intelligent router, to achieve nearby content services for users can effectively reduce backbone traffic and enhance service performance. This article proposes a decentralized PageRank-based content dissemination model at the edge of network, in which a suitable node selection algorithm is designed to distribute the content evenly in the network. Each node can quickly obtain data from neighbor nodes, thereby reducing the cloud load as well as the network bandwidth and improving the service response performance. The simulation shows that, compared with the other two dissemination algorithms, the content is distributed more even, which means every node has more opportunity to obtain the data from neighbors; and the service rejection rate can be decreased by an average of 5.2% in the case of high concurrent requests.

The emergence of the social Internet of things (SIoT) paradigm opens a new path for the development of the next generation of the Internet of things, but it also brings new security risks. The social attributes contained in SIoT exacerbate the harm of the virus to the network, so it is essential to analyze the characteristics of the virus and the core factors that influence the spread of the virus. This paper put forward a virus propagation model (susceptible-propagation ability-exposed-infected-recovered, SPEIR) which mainly considers the differences in intelligent devices’ propagation ability. Then, this paper proposes a novel method(i.e., DKP), which combines K-shell theory that we add the delete order and the idea of PageRank that determines the importance of web pages to calculate intelligent devices’ propagation ability. And the experiments have proved the accuracy of DKP is high.

High-quality answers, usually given by experts, play an important role in community question answering (CQA) websites. Therefore, experts in these websites are defined as those who provide high-quality answers. We propose two semi-supervised learning models based on the graph convolution neural network (GCN) to identify them. Both models comprehensively extract features from the social behavior network, user profiles, and question and answer text. Specifically, we construct a social behavior network according to the co-answering relationship among answerers, which means every two answerers are connected if they answer the same questions. The difference between these two models is the methods of extracting text features. One model named GCN-Doc uses Doc2vec to get text vectors before training. The other model named GCN-Lstm with a long short term memory (LSTM) network extracts text features while training. Experiments using real-world data from Zhihu.com, one of the largest Chinese CQA websites, show that both GCN-Doc and GCN-Lstm can identify experts effectively comparing with baselines of PageRank and other GCN based neural network models. Besides, GCN-Lstm performs better than GCN-Doc.

This article proposes a coded distributed graph processing framework to alleviate the communication bottleneck in large-scale distributed graph processing. In particular, we propose a topology-aware coded computing (TACC) algorithm that has two novel salient features: (i) a topology-aware graph allocation strategy, and (ii) a coded aggregation scheme that combines the intermediate computations for graph processing while constructing coded messages. The proposed setup results in a trade-off between computation and communication, in that increasing the computation load at the distributed parties can in turn reduce the communication load. We demonstrate the effectiveness of the TACC algorithm by comparing the communication load with existing setups on both Erdos-Renyi and Barabasi-Albert type random graphs, as well as real-world Google web graph for PageRank computations. In particular, we show that the proposed coding strategy can lead to up to $82\%$ reduction in communication load and up to $46\%$ reduction in overall execution time, when compared to the state-of-the-art and implemented on the Amazon EC2 cloud compute platform.

Traffic congestion has become increasingly prominent. Effective prediction of road congestion will provide a great reference for urban road planning and residents’ travel. Taxi trajectory data objectively reflect the travel routes of the residents of a city. With good temporal and spatial characteristics and high timeliness, these data have become important in the study of urban spatio-temporal characteristics. In this paper, the PageRank-K clustering algorithm is used to analyse hotspots and cluster hotspots of a region. The shortest-distance matching algorithm based on the transition probability is used for map matching; then, the average speed of the road sections can be calculated, and the direction of taxis can be determined. The Dual_XGBoost model is used for traffic congestion prediction. Finally, we compared our model with similar models. We take roads in Shanghai as the test object. The results show that our method is faster in training, can predict congestion at any time and is more sensitive to long-term features than the other models.

With the rapidly increasing complexity and indispensable status of software systems, unprecedented challenges have been brought to software debugging and fault repair. Among the states of art automated fault localization techniques, spectrum-based fault localization (SBFL) is one of the most widely studied heuristic approaches. While, existing SBFL techniques are mostly focused on the analysis of the test spectrum, which loses sight of the effectiveness implied in interactions of software entities. In this paper, an optimized fault localization approach is proposed, which integrates spectrum-based fault localization and fault influence propagation analysis. The intuition is that the entity associated with more suspicious entities is more likely to be faulty. (1) A dynamic-instrumenting execution tracer is developed to record test coverage data and method call relations simultaneously. (2) The Fault Influence Network (FIN) based on complex network theory is constructed, of which the network topology is abstracted from method call relations and the weights of nodes are calculated applying a raw fault locator. Thus, the test spectrum and the method interactive relations are combined for further collaborative analysis. (3) A PageRank-based fault localization approach is proposed. By the computation of fault influence propagation, the suspiciousness score PR-Susp of the real faulty method can be enhanced significantly. Also, a candidate set pruning based on failing tests is implemented, which is used for the narrowing of investigation. Experiments are conducted over real-world fault dataset Defects4J with 33 raw spectrum-based fault locators, which proves that the proposed approach improves the baseline 14.9% averagely on the metric acc@5 with a growth rate of over 39% on all metrics of acc@1, acc@3, acc@5, MAP, and MWE.

The Bonacich centrality is a well-known measure of the relative importance of nodes in a network. This notion is, for example, at the core of Google's PageRank algorithm. In this paper we study a network formation game where each player corresponds to a node in the network to be formed and can decide how to rewire his m out-links aiming at maximizing his own Bonacich centrality, which is his utility function. We study the Nash equilibria (NE) and the best response dynamics of this game and we provide a complete classification of the set of NE when m=1 and a fairly complete classification of the NE when m=2. Our analysis shows that the centrality maximization performed by each node tends to create undirected and disconnected or loosely connected networks, namely 2-cliques for m=1 and rings or a special "Butterfly"-shaped graph when m=2. Our results build on locality property of the best response function in such game that we formalize and prove in the paper., Comment: 10 pages, 11 figures

Feature selection is a challenging problem that occurs in the high-dimensional data analysis of many major applications. It addresses the curse of dimensionality by determining a small set of features to represent high-dimensional data without significant or noticeable loss of information. The purpose of this study is to develop and investigate a new unsupervised feature selection method which uses the k-influence space concept and subspace learning to map features onto a weighted graph and rank them by importance according to the PageRank graph centrality measure. The graph design in this method promotes feature relevance, downgrades redundancy, and is robust to outliers and cluster imbalances. In K-Means classification experiments using the ASU feature selection testing datasets, the method produces better accuracy and normalized mutual information results than state-of-the-art unsupervised feature selection algorithms. In a further evaluation, using a dataset of over 14,000 tweets, conventional classification of features selected by the method gave better sentiment analysis results than deep learning feature selection and classification.

We propose and evaluate generative models for case law citation networks that account for legal authority, subject relevance, and time decay. Since Common Law systems rely heavily on citations to precedent, case law citation networks present a special type of citation graph which existing models do not adequately reproduce. We describe a general framework for simulating node and edge generation processes in such networks, including a procedure for simulating case subjects, and experiment with four methods of modelling subject relevance: using subject similarity as linear features, as fitness coefficients, constraining the citable graph by subject, and computing subject-sensitive PageRank scores. Model properties are studied by simulation and compared against existing baselines. Promising approaches are then benchmarked against empirical networks from the United States and Singapore Supreme Courts. Our models better approximate the structural properties of both benchmarks, particularly in terms of subject structure. We show that differences in the approach for modelling subject relevance, as well as for normalizing attachment probabilities, produce significantly different network structures. Overall, using subject similarities as fitness coefficients in a sum-normalized attachment model provides the best approximation to both benchmarks. Our results shed light on the mechanics of legal citations as well as the community structure of case law citation networks. Researchers may use our models to simulate case law networks for other inquiries in legal network science.

In this paper we show how re-interpreting PageRank as an argumentation semantics for a bipolar argumentation framework empowers its explainability. After showing that PageRank, naively re-interpreted as an argumentation semantics for support frameworks, fails to satisfy some generally desirable properties, we propose a novel approach able to reconstruct PageRank as a gradual semantics of a suitably defined bipolar argumentation framework, while satisfying these properties. We then show how the theoretical advantages afforded by this approach also enjoy an enhanced explanatory power: we propose several types of argument-based explanations for PageRank, each of which focuses on different aspects of the algorithm and uncovers information useful for the comprehension of its results.

This study explores how supply network degree, closeness, and betweenness centralities affect firm performance, and the moderating effects of organizational reputation (measured by PageRank central...

Given a directed graph G, a source node s, and a target node t, the personalized PageRank (PPR) $$\pi (s,t)$$ measures the importance of node t with respect to node s. In this work, we study the single-source PPR query, which takes a source node s as input and outputs the PPR values of all nodes in G with respect to s. The single-source PPR query finds many important applications, e.g., community detection and recommendation. Deriving the exact answers for single-source PPR queries is prohibitive, so most existing work focuses on approximate solutions. Nevertheless, existing approximate solutions are still inefficient, and it is challenging to compute single-source PPR queries efficiently for online applications. This motivates us to devise efficient parallel algorithms running on shared-memory multi-core systems. In this work, we present how to efficiently parallelize the state-of-the-art index-based solution FORA, and theoretically analyze the complexity of the parallel algorithms. Theoretically, we prove that our proposed algorithm achieves a time complexity of $$O(W/P+\log ^2{n})$$, where W is the time complexity of sequential FORA algorithm, P is the number of processors used, and n is the number of nodes in the graph. FORA includes a forward push phase and a random walk phase, and we present optimization techniques to both phases, including effective maintenance of active nodes, improving the efficiency of memory access, and cache-aware scheduling. Extensive experimental evaluation demonstrates that our solution achieves up to 37$$\times $$ speedup on 40 cores and 3.3$$\times $$ faster than alternatives on 40 cores. Moreover, the forward push alone can be used for local graph clustering, and our parallel algorithm for forward push is 4.8$$\times $$ faster than existing parallel alternatives.

In this paper, a general framework based on the variant Bayesian filter is proposed for the fusion of multi-trackers along with multi-features. Comparing with other fusion based trackers, this framework tries to combine feature-level fusion methods with decision-level fusion methods and obtain the tracking result in a hybrid cascade approach. Firstly, tracking results of multi-trackers are treated as prior knowledge to provide candidate samples, which could reduce the number of samples and avoid the assumption of the Gaussian distribution. Secondly, two novel candidate selection strategies, weighted voting strategy and PageRank based strategy, are proposed and run parallel to measure the similarities among different trackers under different features and achieve the fusion through the proposed correlation matrix pool and decision vectors. Unlike traditional template matching methods, these two strategies consider both the inter similarities (the similarities between template and candidates) and intra similarities (the similarities among candidates). Finally, a novel decision and update strategy with tracklet prediction-comparison is proposed in a cascaded way to cope with the consistency problem of the proposed candidate selection strategies and measure the usability of templates. Based on the proposed framework, a tracker named as MTiB is designed and tested on widely used benchmarks. The experimental results demonstrate that our proposed framework is feasible.

PageRank plays a vital role towards the preparation of the index for web resources. The index is processed by crawling down the relevant websites. Hence, the validation of computed PageRank is crucially significant towards the reliability enhancement of processed indexed list. On the other hand, energy efficiency and the stability of a system are crucially towards the environmental sustainability. In this regard, an energy stability-based PageRank validation model in Cellular Automata is presented in this research which facilitates a very low energy consumption by its physical components. Detailed investigations in view of energy stability explore the role of energy stability towards the validation of PageRank. Hence, an alternative approach towards the validation of PageRank using energy stability is presented in this research. Analytical results obtained with proposed approach for several Clouds further explore its potential capability as a green computing model in the Cloud.

Centrality analysis has been a very important research topic in online social networks. Traditionally, centrality analysis is carried out for a specific network. In this article, we conduct centrality analysis in random networks. For this, we consider the class of $d$ -regular directed acyclic random networks generated by the configuration model. We consider two centralities: the PageRank and the in-component centrality (defined as the size of the in-component of a node). For the PageRank, we derive a closed-form solution in the complete directed acyclic network and show that it can be used as an approximation in a $d$ -regular directed acyclic random network. For the in-component centrality, we derive upper bounds for its first moment and its second moment in a $d$ -regular directed acyclic random network. These results have interesting applications in top- $k$ recommendations. In particular, we propose a qualifying round algorithm that outputs a set of nodes to include the top- $k$ nodes with high probability. Various experiments are conducted to show the effectiveness of the algorithm.