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DTSTART;TZID=Europe/Stockholm:20190614T143000
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UID:submissions.pasc-conference.org_PASC19_sess149_msa287@linklings.com
SUMMARY:Distributed Graph Clustering Using Modularity and Map Equation
DESCRIPTION:Minisymposium\nComputer Science and Applied Mathematics, Emerg
 ing Application Domains, Climate and Weather, Life Sciences\n\nDistributed
  Graph Clustering Using Modularity and Map Equation\n\nHamann, Strasser, W
 agner, Zeitz\n\nWe study large-scale, distributed graph clustering. Given 
 an undirected graph, our objective is to partition the nodes into disjoint
  sets called clusters. A cluster should contain many internal edges while 
 being sparsely connected to other clusters. In the context of a social net
 work, a cluster could be a group of friends. Modularity and map equation a
 re established formalizations of this internally-dense-externally-sparse p
 rinciple. We present two versions of a simple distributed algorithm to opt
 imize both measures. They are based on Thrill, a distributed big data proc
 essing framework that implements an extended MapReduce model. The algorith
 ms for the two measures, DSLM-Mod and DSLM-Map, differ only slightly. Adap
 ting them for similar quality measures is straight-forward. We conduct an 
 extensive experimental study on real-world graphs and on synthetic benchma
 rk graphs with up to 68 billion edges. Our algorithms are fast while 
 detecting clusterings similar to those detected by other sequential, paral
 lel and distributed clustering algorithms. Compared to the distributed Gos
 sipMap algorithm, DSLM-Map needs less memory, is up to an order of ma
 gnitude faster and achieves better quality.
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