Internet Networking

Algorithms and Models for the Web Graph: 11th International by Anthony Bonato, Fan Chung Graham, Pawel Pralat

By Anthony Bonato, Fan Chung Graham, Pawel Pralat

This e-book constitutes the refereed court cases of the eleventh overseas 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 used to be to extra the knowledge of graphs that come up from the net and diverse person actions on the net, and stimulate the advance of high-performance algorithms and purposes that make the most those graphs. The workshop amassed the researchers who're engaged on graph-theoretic and algorithmic points of comparable complicated networks, together with social networks, quotation networks, organic networks, molecular networks, and different networks coming up from the net.

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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 (Lecture Notes in Computer Science)

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In [1] it is adopted the standard unweighted definition with the exception that triangles are weighted by the edge that closes the triangle. In [21] the weight is only considered in the numerator of the definition of clustering coefficient whereas the denominator is the one of the unweighted case. In [31] the weight of a triangle is obtained by multiplying the weights of the edges. Other proposals that are substantially different from our approach can also be found in [14,32]. The study of the clustering coefficient in several classes of random unweighted graphs can be found in [4].

When v T = e(j), where ei (j) = 1 when i = j and ei (j) = 0 when i = j, then both πi (v) and ρi (v) can be interpreted as the relative importance of node i from the perspective of node j. We see at least three applications of the generalized Personalized PageRank. The network sampling process introduced in [6] can be viewed as a particular case of PageRank with a node-dependent restart. We discuss this relation in more detail in Section 4. Secondly, the generalized Personalized PageRank can be applied as a proximity measure between nodes in semi-supervised machine learning [5,16].

Random alpha PageRank. Internet Mathematics 6(2), 189–236 (2010) 16. : 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.

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