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Journal articles on the topic 'Classical PageRank'
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1
Concezzi, Moreno, and Renato Spigler. "ADI Methods for Three-dimensional Fractional Diffusions." International Journal of Computers and Communications 16 (March 8, 2022): 9–12. http://dx.doi.org/10.46300/91013.2022.16.2.
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ADI methods can be generalized to solve numerically multidimensional fractional diffusion equations, which describe fluid flows through porous media better than classical diffusion equations. A new, unconditionally stable, second-order and well balanced in space, third-order in time ADI scheme has been constructed and its convergence accelerated by an extrapolation technique coupled with the PageRank algorithm.
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Zhang, Jinsong, and Xiaozhong Liu. "Citation Oriented AuthorRank for Scientific Publication Ranking." Applied Sciences 12, no. 9 (April 25, 2022): 4345. http://dx.doi.org/10.3390/app12094345.
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It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.
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Zhang, Jinsong, and Xiaozhong Liu. "Citation Oriented AuthorRank for Scientific Publication Ranking." Applied Sciences 12, no. 9 (April 25, 2022): 4345. http://dx.doi.org/10.3390/app12094345.
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It is now generally accepted that an article written by influential authors often deserves a higher ranking in information retrieval. However, it is a challenging task to determine an author’s relative influence since information about the author is, much of the time, inaccessible. Actually, in scientific publications, the author is an important metadata item, which has been widely used in previous studies. In this paper, we bring an optimized AuthorRank, which is a topic-sensitive algorithm calculated by citation context, into citation analysis for testing whether and how topical AuthorRank can replace or enhance classical PageRank for publication ranking. For this purpose, we first propose a PageRank with Priors (PRP) algorithm to rank publications and authors. PRP is an optimized PageRank algorithm supervised by the Labeled Latent Dirichlet Allocation (Labeled-LDA) topic model with full-text information extraction. We then compared four methods of generating an AuthorRank score, looking, respectively, at the first author, the last author, the most famous author, and the “average” author (of a publication). Additionally, two combination methods (Linear and Cobb–Douglas) of AuthorRank and PRP were compared with several baselines. Finally, as shown in our evaluation results, the performance of AuthorRank combined with PRP is better (p < 0.001) than other baselines for publication ranking.
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Cipolla, Stefano, Carmine Di Fiore, and Francesco Tudisco. "Euler-Richardson method preconditioned by weakly stochastic matrix algebras: a potential contribution to Pagerank computation." Electronic Journal of Linear Algebra 32 (February 6, 2017): 254–72. http://dx.doi.org/10.13001/1081-3810.3343.
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Let S be a column stochastic matrix with at least one full row. Then S describes a Pagerank-like random walk since the computation of the Perron vector x of S can be tackled by solving a suitable M-matrix linear system Mx = y, where M = I â Ï A, A is a column stochastic matrix and Ï is a positive coefficient smaller than one. The Pagerank centrality index on graphs is a relevant example where these two formulations appear. Previous investigations have shown that the Euler- Richardson (ER) method can be considered in order to approach the Pagerank computation problem by means of preconditioning strategies. In this work, it is observed indeed that the classical power method can be embedded into the ER scheme, through a suitable simple preconditioner. Therefore, a new preconditioner is proposed based on fast Householder transformations and the concept of low complexity weakly stochastic algebras, which gives rise to an effective alternative to the power method for large-scale sparse problems. Detailed mathematical reasonings for this choice are given and the convergence properties discussed. Numerical tests performed on real-world datasets are presented, showing the advantages given by the use of the proposed Householder-Richardson method.
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Concezzi, Moreno, and Renato Spigler. "An ADI Method for the Numerical Solution of 3D Fractional Reaction-Diffusion Equations." Fractal and Fractional 4, no. 4 (December 14, 2020): 57. http://dx.doi.org/10.3390/fractalfract4040057.
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A numerical method for solving fractional partial differential equations (fPDEs) of the diffusion and reaction–diffusion type, subject to Dirichlet boundary data, in three dimensions is developed. Such fPDEs may describe fluid flows through porous media better than classical diffusion equations. This is a new, fractional version of the Alternating Direction Implicit (ADI) method, where the source term is balanced, in that its effect is split in the three space directions, and it may be relevant, especially in the case of anisotropy. The method is unconditionally stable, second-order in space, and third-order in time. A strategy is devised in order to improve its speed of convergence by means of an extrapolation method that is coupled to the PageRank algorithm. Some numerical examples are given.
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Qi, Xiaogang, Lifang Liu, Guoyong Cai, and Mande Xie. "A Topology Evolution Model Based on Revised PageRank Algorithm and Node Importance for Wireless Sensor Networks." Mathematical Problems in Engineering 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/165136.
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Wireless sensor network (WSN) is a classical self-organizing communication network, and its topology evolution currently becomes one of the attractive issues in this research field. Accordingly, the problem is divided into two subproblems: one is to design a new preferential attachment method and the other is to analyze the dynamics of the network topology evolution. To solve the first subproblem, a revised PageRank algorithm, called Con-rank, is proposed to evaluate the node importance upon the existing node contraction, and then a novel preferential attachment is designed based on the node importance calculated by the proposed Con-rank algorithm. To solve the second one, we firstly analyze the network topology evolution dynamics in a theoretical way and then simulate the evolution process. Theoretical analysis proves that the network topology evolution of our model agrees with power-law distribution, and simulation results are well consistent with our conclusions obtained from the theoretical analysis and simultaneously show that our topology evolution model is superior to the classic BA model in the average path length and the clustering coefficient, and the network topology is more robust and can tolerate the random attacks.
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Ma, Linxiao, Yuzhu Wang, Yue Wang, Ning Li, Sai-Fu Fung, Lu Zhang, and Qian Zheng. "The Hotspots of Sports Science and the Effects of Knowledge Network on Scientific Performance Based on Bibliometrics and Social Network Analysis." Complexity 2021 (May 22, 2021): 1–12. http://dx.doi.org/10.1155/2021/9981202.
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In this study, we sorted out the research hotspots in sports science by bibliometric method and also used social network analysis to explore the relationship between knowledge networks and their scientific performance. We found 38 high-frequency keywords with obvious curricular nature or classical direction of sports science research and 4 high-frequency research groups. The topics of hotspots covered the secondary disciplines of sports science: physical education and training, national traditional sports, sports human science, and sports humanities and sociology. However, sports human science research is less; therefore, accelerating the research of sports human science is the focus of future research. Meanwhile, we use social network structure analysis (i.e., centrality, clustering coefficient, PageRank, and structural holes) to study the relationship between knowledge elements in knowledge networks and their scientific performance. In addition to betweenness centrality, the closeness centrality, clustering coefficient, and structural holes of knowledge elements are significantly and positively related to their influence. In the relationship between knowledge elements and productivity, betweenness centrality and closeness centrality show significant positive correlations, and clustering coefficient and structural hole show significant negative correlations. Therefore, knowledge networks can be used to predict the scientific performance of knowledge elements.
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Valour, D., I. Hue, B. Grimard, and B. Valour. "Gene selection heuristic algorithm for nutrigenomics studies." Physiological Genomics 45, no. 14 (July 15, 2013): 615–28. http://dx.doi.org/10.1152/physiolgenomics.00139.2012.
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Large datasets from -omics studies need to be deeply investigated. The aim of this paper is to provide a new method (LEM method) for the search of transcriptome and metabolome connections. The heuristic algorithm here described extends the classical canonical correlation analysis (CCA) to a high number of variables (without regularization) and combines well-conditioning and fast-computing in “R.” Reduced CCA models are summarized in PageRank matrices, the product of which gives a stochastic matrix that resumes the self-avoiding walk covered by the algorithm. Then, a homogeneous Markov process applied to this stochastic matrix converges the probabilities of interconnection between genes, providing a selection of disjointed subsets of genes. This is an alternative to regularized generalized CCA for the determination of blocks within the structure matrix. Each gene subset is thus linked to the whole metabolic or clinical dataset that represents the biological phenotype of interest. Moreover, this selection process reaches the aim of biologists who often need small sets of genes for further validation or extended phenotyping. The algorithm is shown to work efficiently on three published datasets, resulting in meaningfully broadened gene networks.
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Fafalios, Pavlos, Panagiotis Papadakos, and Yannis Tzitzikas. "Enriching Textual Search Results at Query Time Using Entity Mining, Linked Data and Link Analysis." International Journal of Semantic Computing 08, no. 04 (December 2014): 515–44. http://dx.doi.org/10.1142/s1793351x14400170.
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The integration of the classical Web (of documents) with the emerging Web of Data is a challenging vision. In this paper we focus on an integration approach during searching which aims at enriching the responses of non-semantic search systems with semantic information, i.e. Linked Open Data (LOD), and exploiting the outcome for offering advanced exploratory search services which provide an overview of the search space and allow the users to explore the related LOD. We use named entities identified in the search results for automatically connecting search hits with LOD and we consider a scenario where this entity-based integration is performed at query time with no human effort and no a-priori indexing which is beneficial in terms of configurability and freshness. However, the number of identified entities can be high and the same is true for the semantic information about these entities that can be fetched from the available LOD. To this end, in this paper we propose a Link Analysis-based method which is used for ranking (and thus selecting to show) the more important semantic information related to the search results. We report the results of a survey regarding the marine domain with promising results, and comparative results that illustrate the effectiveness of the proposed (PageRank-based) ranking scheme. Finally, we report experimental results regarding efficiency showing that the proposed functionality can be offered even at query time.
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Boldi, Paolo, Flavio Furia, and Sebastiano Vigna. "Monotonicity in undirected networks." Network Science, February 2, 2023, 1–23. http://dx.doi.org/10.1017/nws.2022.42.
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Abstract Is it always beneficial to create a new relationship (have a new follower/friend) in a social network? This question can be formally stated as a property of the centrality measure that defines the importance of the actors of the network. Score monotonicity means that adding an arc increases the centrality score of the target of the arc; rank monotonicity means that adding an arc improves the importance of the target of the arc relatively to the remaining nodes. It is known that most centralities are both score and rank monotone on directed, strongly connected graphs. In this paper, we study the problem of score and rank monotonicity for classical centrality measures in the case of undirected networks: in this case, we require that score, or relative importance, improves at both endpoints of the new edge. We show that, surprisingly, the situation in the undirected case is very different, and in particular that closeness, harmonic centrality, betweenness, eigenvector centrality, Seeley’s index, Katz’s index, and PageRank are not rank monotone; betweenness and PageRank are not even score monotone. In other words, while it is always a good thing to get a new follower, it is not always beneficial to get a new friend.
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Boito, Paola, and Roberto Grena. "Quantum hub and authority centrality measures for directed networks based on continuous-time quantum walks." Journal of Complex Networks 9, no. 6 (October 20, 2021). http://dx.doi.org/10.1093/comnet/cnab038.
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Abstract In this article, we introduce, test and discuss three quantum methods for computing hub and authority centrality scores in directed networks. The methods are based on unitary, continuous-time quantum walks; the construction of a suitable Hermitian Hamiltonian is achieved by performing a quantum walk on the associated bipartite graph. Two methods, called CQAu and CQAw, use the same evolution operator, inspired by the classical Hyperlink-Induced Topic Search (HITS) algorithm, but with different initial states; the computation of hub and authority scores is performed simultaneously. The third method, called CQG and inspired by classical PageRank, requires instead two separate runs with different evolution operators, one for hub and one for authority scores. The methods are tested on several directed graphs with different sizes and properties; a comparison with other well-established ranking algorithms is provided. CQAw emerges as the most reliable of the three methods and yields rankings that are largely compatible with results from HITS, although CQAu and CQG also present interesting features and potential for applications.
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Wang, Guishen, Kaitai Wang, Hongmei Wang, Huimin Lu, Xiaotang Zhou, and Yuncong Feng. "Uncovering local community structure on line graph through degree centrality and expansion." International Journal of Modern Physics B, April 24, 2021, 2150120. http://dx.doi.org/10.1142/s0217979221501204.
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Local community detection algorithms are an important type of overlapping community detection methods. Local community detection methods identify local community structure through searching seeds and expansion process. In this paper, we propose a novel local community detection method on line graph through degree centrality and expansion (LCDDCE). We firstly employ line graph model to transfer edges into nodes of a new graph. Secondly, we evaluate edges relationship through a novel node similarity method on line graph. Thirdly, we introduce local community detection framework to identify local node community structure of line graph, combined with degree centrality and PageRank algorithm. Finally, we transfer them back into original graph. The experimental results on three classical benchmarks show that our LCDDCE method achieves a higher performance on normalized mutual information metric with other typical methods.
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O’Brien, Joseph D., and James P. Gleeson. "A complex networks approach to ranking professional Snooker players." Journal of Complex Networks 8, no. 6 (December 1, 2020). http://dx.doi.org/10.1093/comnet/cnab003.
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Abstract A detailed analysis of matches played in the sport of Snooker during the period 1968–2020 is used to calculate a directed and weighted dominance network based upon the corresponding results. We consider a ranking procedure based upon the well-studied PageRank algorithm that incorporates details of not only the number of wins a player has had over their career but also the quality of opponent faced in these wins. Through this study, we find that John Higgins is the highest performing Snooker player of all time with Ronnie O’Sullivan appearing in second place. We demonstrate how this approach can be applied across a variety of temporal periods in each of which we may identify the strongest player in the corresponding era. This procedure is then compared with more classical ranking schemes. Furthermore, a visualization tool known as the rank-clock is introduced to the sport which allows for immediate analysis of the career trajectory of individual competitors. These results further demonstrate the use of network science in the quantification of success within the field of sport.
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Loke, T., J. W. Tang, J. Rodriguez, M. Small, and J. B. Wang. "Comparing classical and quantum PageRanks." Quantum Information Processing 16, no. 1 (December 19, 2016). http://dx.doi.org/10.1007/s11128-016-1456-z.
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Chen, Nie. "E-Commerce Brand Ranking Algorithm Based on User Evaluation and Sentiment Analysis." Frontiers in Psychology 13 (June 23, 2022). http://dx.doi.org/10.3389/fpsyg.2022.907818.
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ObjectiveConsumers often need to compare the same type of products from different merchants to determine their purchasing needs. Fully mining the product information on the website and applying it to e-commerce websites or product introduction websites can not only allow consumers to buy products that are more in line with their wishes, but also help merchants understand user needs and the advantages of each product. How to quantify the emotional tendency of evaluation information and how to recommend satisfactory products to consumers is the research purpose of this paper.MethodAccording to the analysis of the research object, this paper uses the Python crawler library to efficiently crawl the data required for research. By writing a custom program for crawling, the resulting data is more in line with the actual situation. This paper uses the BeautifulSoup library in Python web crawler technology for data acquisition. Then, in order to ensure high-quality data sets, the acquired data needs to be cleaned and deduplicated. Finally, preprocessing such as sentence segmentation, word segmentation, and semantic analysis is performed on the cleaned data, and the data format required by the subsequent model is output. For weightless network, the concept of node similarity is proposed, which is used to measure the degree of mutual influence between nodes. Combined with the LeaderRank algorithm, and fully considering the differences between nodes in the interaction, the SRank algorithm is proposed. Different from the classical node importance ranking method, the SRank algorithm fully considers the local and global characteristics of nodes, which is more in line with the actual network.Results/DiscussionThis paper calculates the sentiment polarity of users’ comments, obtains the final user influence ranking, and identifies opinion leaders. The final ranking results were compared and analyzed with the traditional PageRank algorithm and SRank ranking algorithm, and it was found that the opinion leaders identified by the opinion leader identification model integrating user activity and comment sentiment were more reasonable and objective. The algorithm in this paper improves the efficiency of operation to a certain extent, and at the same time solves the problem that sentiment analysis cannot be effectively used in social network analysis, and increases the accuracy of e-commerce brand ranking.