Fast Incremental and Personalized PageRank

In this paper, the authors analyze the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, and similar random walk based methods (with focus on SALSA), on large-scale dynamically evolving social networks. They assume that the graph of friendships is stored in distributed shared memory, as is the case for large social networks such as Twitter. For global PageRank, they assume that the social network has n nodes, and m adversarially chosen edges arrive in a random order.

Provided by: VLD Digital Topic: Data Management Date Added: Sep 2011 Format: PDF

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