Conference Proceedings

Unifying the Global and Local Approaches: An Efficient Power Iteration with Forward Push

H Wu, J Gan, Z Wei, R Zhang

Proceedings of the ACM SIGMOD International Conference on Management of Data | ACM | Published : 2021


Personalized PageRank (PPR) is a critical measure of the importance of a node t to a source node s in a graph. The Single-Source PPR (SSPPR) query computes the PPR's of all the nodes with respect to s on a directed graph G with n nodes and m edges; and it is an essential operation widely used in graph applications. In this paper, we propose novel algorithms for answering two variants of SSPPR queries: (i) high-precision queries and (ii) approximate queries. For high-precision queries, Power Iteration (PowItr) and Forward Push (FwdPush) are two fundamental approaches. Given an absolute error threshold λ (which is typically set to as small as 10-8), the only known bound of FwdPush is O(m/λ), m..

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