Titlebook: Algorithms and Models for the Web Graph; 9th International Wo Anthony Bonato,Jeannette Janssen Conference proceedings 2012 Springer-Verlag

严峻

书目名称Algorithms and Models for the Web Graph影响因子(影响力)




书目名称Algorithms and Models for the Web Graph影响因子(影响力)学科排名




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书目名称Algorithms and Models for the Web Graph网络公开度学科排名




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书目名称Algorithms and Models for the Web Graph被引频次学科排名




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Commission

斜谷

A Game-Theoretic Model of Attention in Social Networks, construct and analyze a family of production games that have an arbitrarily large price of anarchy. We also prove non-robustness of the price of anarchy for a particular instance of the introduced family, establishing a distinction between the games studied here and network congestion games.

enormous

Mutual or Unrequited Love: Identifying Stable Clusters in Social Networks with Uni- and Bi-directio. mutuality tendency and minimizing the . mutuality tendency. Extensive simulation results on synthetic datasets as well as real online social network datasets such as Slashdot, demonstrate that our proposed mutuality-tendency-aware spectral clustering algorithm extracts more stable social community

Adjourn

A Fast Algorithm to Find All High Degree Vertices in Graphs with a Power Law Degree Sequence, the entire graph .(.) by the biassed walk is .. Thus the expected time to discover all vertices by the biassed walk is not much higher than in the case of a simple random walk Θ(. log.)..The standard preferential attachment process generates graphs with power law . = 3. Choosing search parameter . 

compassion

上坡

Neurophysiologische Mechanismen im Schlafhis problem must have expected time complexity of .. Thus, our algorithm is optimal up to logarithmic factors. Our algorithm (for identifying vertices with significant PageRank) applies a multi-scale sampling scheme that uses a fast personalized PageRank estimator as its main subroutine. For that, w

laceration

效果

M. Hennerici,M. Daffertshofer,S. Meairs. mutuality tendency and minimizing the . mutuality tendency. Extensive simulation results on synthetic datasets as well as real online social network datasets such as Slashdot, demonstrate that our proposed mutuality-tendency-aware spectral clustering algorithm extracts more stable social community

固定某物

,Spontane intrazerebrale Hämatome, the entire graph .(.) by the biassed walk is .. Thus the expected time to discover all vertices by the biassed walk is not much higher than in the case of a simple random walk Θ(. log.)..The standard preferential attachment process generates graphs with power law . = 3. Choosing search parameter .