By Abraham D. Flaxman, Juan Vera (auth.), Anthony Bonato, Fan R. K. Chung (eds.)
This e-book constitutes the refereed complaints of the fifth overseas Workshop on Algorithms and versions for the Web-Graph, WAW 2007, held in San Diego, CA, united states, in December 2007 - colocated with WINE 2007, the 3rd foreign Workshop on net and community Economics.
The thirteen revised complete papers and 5 revised brief papers awarded have been conscientiously reviewed and chosen from a wide pool of submissions for inclusion within the booklet. The papers handle a wide selection of subject matters on the topic of the research of the Web-graph akin to random graph versions for the Web-graph, PageRank research and computation, decentralized seek, neighborhood partitioning algorithms, and traceroute sampling.
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Additional info for Algorithms and Models for the Web-Graph: 5th International Workshop, WAW 2007, San Diego, CA, USA, December 11-12, 2007. Proceedings
Of course, a drastic reduction of c also considerably accelerates the computation of PageRank by numerical methods [23,5,24]. Acknowledgments This work is supported by EGIDE ECO-NET grant no. 10191XC and by NWO Meervoud grant no. 401. References 1. : The PageRank citation ranking: Bringing order to the Web. Technical report, Stanford University (1998) 2. : Authoritative sources in a hyperlinked environment. Journal of the ACM 46(5), 604–632 (1999) 3. : The stochastic approach for link-structure analysis (SALSA) and the TKC eﬀect.
Topic-sensitive PageRank: A context-sensitive ranking algorithm for Web search. IEEE Transactions on Knowledge and Data Engineering 15(4), 784–796 (2003) 19. : Numerical Computing with MATLAB. In: SIAM (2003) 20. : Ranking the Web frontier. In: WWW 2004: Proceedings of the 13th international conference on World Wide Web, pp. 309–318. ACM Press, New York (2004) 21. : Non-negative Matrices and Markov Chains. Springer Series in Statistics. Springer, New York, Revised reprint of the second (1981) edition [Springer-Verlag, New York MR0719544] (2006) 22.
The density of GreedyMaxClique is obviously 1, by the algorithm definition. We can conclude that the practical results of the JellyCore algorithm, on the real AS graph, agree extremely well with the results of both kCore and GreedyMaxClique. 12000 Execution time [mSec] 10000 kCore JellyCore GreedyMaxClique 8000 6000 4000 2000 0 11,000 13,000 15,000 17,000 Number of vertices 19,000 21,000 Fig. 3. 2 Execution Times Figure 3 shows that the running times of JellyCore and GreedyMaxClique are almost identical, and that kCore is indeed slower: Jellycore runs about 6 times faster than kCore on the largest AS graphs.