  The detection of communities in graph datasets provides insight about a
graph's underlying structure and is an important tool for various domains such
as social sciences, marketing, traffic forecast, and drug discovery. While most
existing algorithms provide fast approaches for community detection, their
results usually contain strictly separated communities. However, most datasets
would semantically allow for or even require overlapping communities that can
only be determined at much higher computational cost. We build on an efficient
algorithm, Fox, that detects such overlapping communities. Fox measures the
closeness of a node to a community by approximating the count of triangles
which that node forms with that community. We propose LazyFox, a multi-threaded
version of the Fox algorithm, which provides even faster detection without an
impact on community quality. This allows for the analyses of significantly
larger and more complex datasets. LazyFox enables overlapping community
detection on complex graph datasets with millions of nodes and billions of
edges in days instead of weeks. As part of this work, LazyFox's implementation
was published and is available as a tool under an MIT licence at
https://github.com/TimGarrels/LazyFox.
