By Anthony Bonato, Fan Chung Graham, Pawel Pralat
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.
Read or Download Algorithms and Models for the Web Graph: 11th International Workshop, WAW 2014, Beijing, China, December 17-18, 2014, Proceedings PDF
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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
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) .
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