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Fast Incremental and Personalized PageRank
| Content Provider | Semantic Scholar |
|---|---|
| Author | Bahmani, Bahman Goel, Ashish |
| Copyright Year | 2010 |
| Abstract | In this paper, we 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. We 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, we assume that the social network has n nodes, and m adversarially chosen edges arrive in a random order. We show that with a reset probability of ǫ, the total work needed to maintain an accurate estimate (using the Monte Carlo method) of the PageRank of every node at all times is O( lnm ǫ ). This is significantly better than all known bounds for incremental PageRank. For instance, if we naively recompute the PageRanks as each edge arrives, the simple power iteration method needs Ω( m 2 |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | https://lanl.arxiv.org/pdf/1006.2880 |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
