Finding Dense Subgraph for Community Detection on Social Network Based on Information Diffusion

Venica, Liptia; Saptawati, Gusti Ayu Putri

doi:10.1109/icodse53690.2021.9648502

Cited by 1 publication

(1 citation statement)

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“…Dense subgraph and substructure detection is a fundamental tool in graph mining, with many applications in areas including social network analysis [13,60], fraud detection [24], and computational biology [3,21]. The recent work of Sariyüce et al [52] introduced the nucleus decomposition problem, a generalization of the 𝑘-core [45,53] and 𝑘-truss [12] problems which better captures higher-order structures in graphs.…”

Section: Introductionmentioning

confidence: 99%

Theoretically and practically efficient parallel nucleus decomposition

Shi¹,

Dhulipala²,

Shun³

2021

Proc. VLDB Endow.

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This paper studies the nucleus decomposition problem, which has been shown to be useful in finding dense substructures in graphs. We present a novel parallel algorithm that is efficient both in theory and in practice. Our algorithm achieves a work complexity matching the best sequential algorithm while also having low depth (parallel running time), which significantly improves upon the only existing parallel nucleus decomposition algorithm (Sariyüce et al. , PVLDB 2018). The key to the theoretical efficiency of our algorithm is a new lemma that bounds the amount of work done when peeling cliques from the graph, combined with the use of a theoretically-efficient parallel algorithms for clique listing and bucketing. We introduce several new practical optimizations, including a new multi-level hash table structure to store information on cliques space-efficiently and a technique for traversing this structure cache-efficiently. On a 30-core machine with two-way hyper-threading on real-world graphs, we achieve up to a 55x speedup over the state-of-the-art parallel nucleus decomposition algorithm by Sariyüce et al. , and up to a 40x self-relative parallel speedup. We are able to efficiently compute larger nucleus decompositions than prior work on several million-scale graphs for the first time.

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