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UID:submissions.pasc-conference.org_PASC24_sess124_msa109@linklings.com
SUMMARY:Faster Local Motif Clustering via Maximum Flows
DESCRIPTION:Minisymposium\n\nAdil Chhabra, Marcelo Fonseca Faraj, and Chri
 stian Schulz (University of Heidelberg)\n\nLocal clustering aims to identi
 fy 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 wel
 l-characterized, i.e., in the context of motifs, such as edges or triangle
 s, it should have a high number of internal motifs and a low number of ext
 ernal motifs (motif conductance). In this talk, we present SOCIAL, a state
 -of-the-art algorithm for local motif clustering which optimizes for motif
  conductance based on a local hypergraph model representation of the probl
 em and an adapted version of the max-flow quotient-cut improvement algorit
 hm (MQI). In our experiments with the triangle motif, SOCIAL produces loca
 l clusters with superior solution quality among alternate approaches, whil
 e being up to multiple orders of magnitude faster.\n\nDomain: Computationa
 l Methods and Applied Mathematics\n\nSession Chairs: Dimosthenis Pasadakis
  (Università della Svizzera italiana) and Olaf Schenk (Università della Sv
 izzera italiana, ETH Zurich)
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