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. * Work done while interning at Twitter † Work done while at Twitter.
For instance, REF proposes a Monte Carlo technique to perform fast computation of random walk based algorithms such as PageRank.
Fast Incremental and Personalized PageRank
PVLDB
PVLDB
Mathematics Computer Science