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We study large-scale, distributed graph clustering. Given an undirectedgraph, our objective is to partition the nodes into disjoint sets calledclusters. A cluster should contain many internal edges while being sparselyconnected to other clusters. In the context of a social network, a clustercould be a group of friends. Modularity and map equation are establishedformalizations of this internally-dense-externally-sparse principle. We presenttwo versions of a simple distributed algorithm to optimize both measures. Theyare based on Thrill, a distributed big data processing framework thatimplements an extended MapReduce model. The algorithms for the two measures,DSLM-Mod and DSLM-Map, differ only slightly. Adapting them for similar qualitymeasures is straight-forward. We conduct an extensive experimental study onreal-world graphs and on synthetic benchmark graphs with up to 68 billionedges. Our algorithms are fast while detecting clusterings similar to thosedetected by other sequential, parallel and distributed clustering algorithms.Compared to the distributed GossipMap algorithm, DSLM-Map needs less memory, isup to an order of magnitude faster and achieves better quality.

Distributed Graph Clustering using Modularity and Map Equation