Algorithms and Models for the Web Graph: 12th International by David F. Gleich, Júlia Komjáthy, Nelly Litvak

By David F. Gleich, Júlia Komjáthy, Nelly Litvak

This booklet constitutes the complaints of the twelfth foreign Workshop on Algorithms and versions for the internet Graph, WAW 2015, held in Eindhoven, The Netherlands, in December 2015.

The 15 complete papers awarded during this quantity have been conscientiously reviewed and chosen from 24 submissions. they're geared up in topical sections named: houses of huge graph types, dynamic approaches on huge graphs, and homes of PageRank on huge graphs.

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Additional resources for Algorithms and Models for the Web Graph: 12th International Workshop, WAW 2015, Eindhoven, The Netherlands, December 10-11, 2015, Proceedings (Lecture Notes in Computer Science)

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If for any c ≥ 1 the event occurs with probability at least 1 − O(n−c ). ). 1 Random Intersection Graphs A wide variety of random intersection graph models have been defined in the literature; in this paper, we restrict our attention to the most well-studied of these, G(n, m, p), which is defined as follows: Definition 1 (Random Intersection Graph Model). Fix positive constants α, β and γ. Let B be a random bipartite graph on parts of size n and m = βnα with each edge present independently with probability p = γn−(1+α)/2 .

T t t2 This concludes the proof of Theorem 5. Local Clustering Coefficient in Generalized Preferential Attachment Models 5 27 Conclusion In this paper, we study the local clustering coefficient C(d) for the vertices of degree d in the T-subclass of the PA-class of models. Despite the fact that the T-subclass generalizes many different models, we are able to analyze the local clustering coefficient for all these models. Namely, we proved that C(d) 2D · d−1 . In particular, this result implies that one asymptotically decreases as Am cannot change the exponent −1 by varying the parameters A, D, and m.

Nature 393, 440–442 (1998) 25. : Maximal planar networks with large clustering coefficient and power-law degree distribution. Phys. Rev. E 71(4), 046141 (2005) Hyperbolicity, Degeneracy, and Expansion of Random Intersection Graphs Matthew Farrell1 , Timothy D. Goodrich2 , Nathan Lemons3 , Felix Reidl4 , Fernando S´ anchez Villaamil4 , and Blair D. de Abstract. We establish the conditions under which several algorithmically exploitable structural features hold for random intersection graphs, a natural model for many real-world networks where edges correspond to shared attributes.

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