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GVE-LPA: Fast Label Propagation Algorithm (LPA) for Community Detection in Shared Memory Setting
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Community detection is the problem of identifying natural divisions in networks. Efficient parallel algorithms for this purpose are crucial in various applications, particularly as datasets grow to substantial scales. This technical report presents an optimized parallel implementation of the Label Propagation Algorithm (LPA), a high speed community detection method, for shared memory multicore systems. On a server equipped with dual 16-core Intel Xeon Gold 6226R processors, our LPA, which we term as GVE-LPA, outperforms FLPA, igraph LPA, and NetworKit LPA by 139x, 97000x, and 40x respectively - achieving a processing rate of 1.4B edges/s on a 3.8B edge graph. In addition, GVE-LPA scales at a rate of 1.7x every doubling of threads.
Forward citations
Cited by 2 Pith papers
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Memory Efficient GPU-based Label Propagation Algorithm (LPA) for Community Detection on Large Graphs
Replacing per-vertex hash tables with 8-slot Misra-Gries sketches makes GPU label propagation use O(|V|) memory instead of O(|E|), cutting memory up to 98x with roughly 5% modularity loss.
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$\nu$-LPA: Fast GPU-based Label Propagation Algorithm (LPA) for Community Detection
A GPU label propagation algorithm reaches 3B edges per second and claims large speedups over existing CPU and GPU community detection tools while producing slightly lower modularity.
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