Md Abdul Motaleb Faysal
Shaikh Arifuzzaman
ABSTRACT
Finding cohesive subgraphs is a crucial graph analysis kernel widely used for social and biological networks (graphs). There exist various approaches for discovering insightful substructures in a network, such as finding cliques, community discovery, and truss decomposition. Finding cliques is a computationally intractable problem, making it difficult to identify cohesive subgraphs in large graphs. One possible solution is k-truss decomposition, which is a relaxed form of finding cliques that can be solved in polynomial time. Further, unlike global community detection–which focuses on breaking down the entire graph into disjoint communities–a local or goal-oriented community search aims at finding the community of an entity of interest. In this work, we identify a k-truss-induced community discovery technique that can detect local communities in polynomial time. However, most previous studies have explored k-truss-induced local community formation in a serial setting, making them unsuitable for large graphs. In this paper, we design a parallel k-truss-induced local community construction method using multi-core parallelism. To the best of our knowledge, this is the first attempt to parallelize this algorithmic approach with extensive performance analysis. Our experiments demonstrate a significant performance improvement, with speedups from 19x to 55x for graphs with hundreds of millions to billions of edges, using NERSC Perlmutter compute nodes.
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Index Terms
- Fast Parallel Index Construction for Efficient K-truss-based Local Community Detection in Large Graphs
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