Practical Parallel Algorithms for Near-Optimal Densest Subgraphs on Massive Graphs.
CoRR(2023)
摘要
The densest subgraph problem has received significant attention, both in
theory and in practice, due to its applications in problems such as community
detection, social network analysis, and spam detection. Due to the high cost of
obtaining exact solutions, much attention has focused on designing approximate
densest subgraph algorithms. However, existing approaches are not able to scale
to massive graphs with billions of edges.
In this paper, we introduce a new framework that combines approximate densest
subgraph algorithms with a pruning optimization. We design new parallel
variants of the state-of-the-art sequential Greedy++ algorithm, and plug it
into our framework in conjunction with a parallel pruning technique based on
$k$-core decomposition to obtain parallel $(1+\varepsilon)$-approximate densest
subgraph algorithms. On a single thread, our algorithms achieve
$2.6$--$34\times$ speedup over Greedy++, and obtain up to $22.37\times$ self
relative parallel speedup on a 30-core machine with two-way hyper-threading.
Compared with the state-of-the-art parallel algorithm by Harb et al.
[NeurIPS'22], we achieve up to a $114\times$ speedup on the same machine.
Finally, against the recent sequential algorithm of Xu et al. [PACMMOD'23], we
achieve up to a $25.9\times$ speedup. The scalability of our algorithms enables
us to obtain near-optimal density statistics on the hyperlink2012 (with roughly
113 billion edges) and clueweb (with roughly 37 billion edges) graphs for the
first time in the literature.
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关键词
massive subgraphs,practical parallel algorithms,near-optimal
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