Affiliation:
1. Renmin University of China, Beijing, China
2. University of Melbourne, Melbourne, Australia
3. Beijing Institute of Technology, Beijing, China
Abstract
Personalized PageRank (PPR) is a popular node proximity metric in graph mining and network research. A single-source PPR (SSPPR) query asks for the PPR value of each node on the graph. Due to its importance and wide applications, decades of efforts have been devoted to the efficient processing of SSPPR queries. Among existing algorithms,
LocalPush
is a fundamental method for SSPPR queries and serves as a cornerstone for subsequent algorithms. In
LocalPush
, a
push
operation is a crucial primitive operation, which distributes the probability at a node
u
to ALL
u
's neighbors via the corresponding edges. Although this
push
operation works well on
unweighted
graphs, unfortunately, it can be rather inefficient on
weighted
graphs. In particular, on
unbalanced
weighted graphs where only a few of these edges take the majority of the total weight among them, the
push
operation would have to distribute "insignificant" probabilities along those edges which just take the minor weights, resulting in expensive overhead.
To resolve this issue, in this paper, we propose the
EdgePush
algorithm, a novel method for computing SSPPR queries on weighted graphs.
EdgePush
decomposes the aforementioned push operations in
edge-based push
, allowing the algorithm to operate at the edge level granularity. As a result, it can flexibly distribute the probabilities according to edge weights. Furthermore, our
EdgePush
allows a fine-grained termination threshold for each individual edge, leading to a superior complexity over
LocalPush.
Notably, we prove that
EdgePush
improves the theoretical query cost of
LocalPush
by an order of up to
O
(
n
) when the graph's weights are
unbalanced.
Our experimental results demonstrate that
EdgePush
significantly outperforms state-of-the-art baselines in terms of query efficiency on large motif-based and real-world weighted graphs.
Publisher
Association for Computing Machinery (ACM)
Subject
General Earth and Planetary Sciences,Water Science and Technology,Geography, Planning and Development
Reference68 articles.
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