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The detection of communities in graph datasets provides insight about agraph's underlying structure and is an important tool for various domains suchas social sciences, marketing, traffic forecast, and drug discovery. While mostexisting algorithms provide fast approaches for community detection, theirresults usually contain strictly separated communities. However, most datasetswould semantically allow for or even require overlapping communities that canonly be determined at much higher computational cost. We build on an efficientalgorithm, Fox, that detects such overlapping communities. Fox measures thecloseness of a node to a community by approximating the count of triangleswhich that node forms with that community. We propose LazyFox, a multi-threadedversion of the Fox algorithm, which provides even faster detection without animpact on community quality. This allows for the analyses of significantlylarger and more complex datasets. LazyFox enables overlapping communitydetection on complex graph datasets with millions of nodes and billions ofedges in days instead of weeks. As part of this work, LazyFox's implementationwas published and is available as a tool under an MIT licence athttps://github.com/TimGarrels/LazyFox.

LazyFox: Fast and parallelized overlapping community detection in large graphs