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What is Fast Incremental and Personalized Pagerank about?

This paper analyzes the efficiency of Monte Carlo methods for incremental computation of PageRank, personalized PageRank, on large-scale dynamically evolving social networks. For global PageRank, we assume that the social network has n nodes, and m adversarially chosen edges arrive in a random order. With a reset probability of, the expected total work needed to maintain an accurate estimate of the PageRank of every node at all times is O( n ln m) this is significantly 2 better than all

Author

moveoninlife

Language

EN