We present new algorithms for Personalized PageRank estimation and Personalized
PageRank search. First, for the problem of estimating Personalized PageRank (PPR) from a …

Given a graph G, a source node s and a target node t, the personalized PageRank (PPR) of t
with respect to s is the probability that a random walk starting from s terminates at t. A single …

Most methods for Personalized PageRank (PPR) precompute and store all accurate PPR
vectors, and at query time, return the ones of interest directly. However, the storage and …

Personalized PageRank (PPR) computation is a fundamental operation in web search,
social networks, and graph analysis. Given a graph G, a source s, and a target t, the PPR …

We present new, more efficient algorithms for estimating random walk scores such as
Personalized PageRank from a given source node to one or several target nodes. These …

Personalized PageRank (PPR) has wide applications in search engines, social
recommendations, community detection, and so on. Nowadays, graphs are becoming …

We propose a new algorithm, FAST-PPR, for computing personalized PageRank: given start
node s and target node t in a directed graph, and given a threshold δ, it computes the …

Personalized PageRank (PPR) has been successfully applied to various applications. In
real applications, it is important to set PPR parameters in an ad-hoc manner when finding …

Personalized PageRank expresses link-based page quality around userselected pages in a
similar way as PageRank expresses quality over the entire web. Existing personalized …

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 …