Read e-book online Algorithms and Models for the Web Graph: 11th International PDF

By Anthony Bonato, Fan Chung Graham, Pawel Pralat

ISBN-10: 3319131222

ISBN-13: 9783319131221

ISBN-10: 3319131230

ISBN-13: 9783319131238

This publication constitutes the refereed complaints of the eleventh foreign Workshop on Algorithms and versions for the internet Graph, WAW 2014, held in Beijing, China, in December 2014.
The 12 papers offered have been rigorously reviewed and chosen for inclusion during this quantity. the purpose of the workshop was once to extra the knowledge of graphs that come up from the net and numerous person actions on the net, and stimulate the improvement of high-performance algorithms and purposes that take advantage of those graphs. The workshop accrued the researchers who're engaged on graph-theoretic and algorithmic facets of comparable advanced networks, together with social networks, quotation networks, organic networks, molecular networks, and different networks coming up from the Internet.

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Additional info for Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings

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An experimental investigation of kernels on graphs for collaborative recommendation and semi-supervised classification. Neural Networks 31, 53–72 (2012) 17. : Topic-Sensitive PageRank. In: Proceedings of WWW 2002 (2002) 18. : Random Graphs and Complex Networks, Lecture notes in preparation (2014) (preprint). nl/∼rhofstad/NotesRGCN. html 19. : Co-authorship networks in the digital library research community. Information Processing & Management 41, 1462–1480 (2005) 20. : Trust-aware recommender systems.

C Springer International Publishing Switzerland 2014 A. Bonato et al. ): WAW 2014, LNCS 8882, pp. 34–46, 2014. 1007/978-3-319-13123-8 4 Efficient Computation of the Weighted Clustering Coefficient 35 Computing the clustering coefficient of a network is a challenging computational task since it reduces to counting the number of triangles in a graph. This task can be naively executed in O(n3 ) time or it can be reduced to matrix multiplication. The problem of computing the local clustering coefficient for every node of the network is even more challenging.

The case of undirected graphs corresponds to the case when W is a symmetric matrix. In general, Dii is the out-degree of node i ∈ V . If some node does not have outgoing edges, we add artificial outgoing edges from that node to all the other nodes. Throughout the paper, we assume that the graph is strongly connected, that is, each node can be reached from any other node. We propose two generalizations of the Personalized PageRank with nodedependent restart: Definition 1 (Occupation-Time Personalized PageRank, OT PPR) .

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Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings by Anthony Bonato, Fan Chung Graham, Pawel Pralat


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