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Faster Local Motif Clustering via Maximum Flows

Authors Adil Chhabra , Marcelo Fonseca Faraj , Christian Schulz

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  • Theory of computation → Design and analysis of algorithms
Keywords
  • local motif clustering
  • motif conductance
  • maximum flows
  • max-flow quotient-cut improvement

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Abstract

Local clustering aims to identify a cluster within a given graph that includes a designated seed node or a significant portion of a group of seed nodes. This cluster should be well-characterized, i.e., it has a high number of internal edges and a low number of external edges. In this work, we propose SOCIAL, a novel algorithm for local motif clustering which optimizes for motif conductance based on a local hypergraph model representation of the problem and an adapted version of the max-flow quotient-cut improvement algorithm (MQI). In our experiments with the triangle motif, SOCIAL produces local clusters with an average motif conductance 1.7% lower than the state-of-the-art, while being up to multiple orders of magnitude faster.

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Adil Chhabra, Marcelo Fonseca Faraj, and Christian Schulz. Faster Local Motif Clustering via Maximum Flows. In 31st Annual European Symposium on Algorithms (ESA 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 274, pp. 34:1-34:16, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023) https://doi.org/10.4230/LIPIcs.ESA.2023.34

Author Details

Adil Chhabra
  • Heidelberg University, Germany
Marcelo Fonseca Faraj
  • Heidelberg University, Germany
Christian Schulz
  • Heidelberg University, Germany

Funding

DFG grant SCHU 2567/5-1.

Supplementary Materials

References

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