GraB: Graph Benchmark for Heterogeneous Graph Clustering

Malik Sebastian Stær Knudsen, Laurits Almskou Brodal, Peter Kristoffer Peczalski, Atefeh Moradan, Davide Mottin, Ira Assent

LOG 2022

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GraB introduces new datasets for overlapping community detection on heterogeneous, attributed networks

GraB introduces new datasets for overlapping community detection on heterogeneous, attributed networks

TL;DR

We introduce GraB, a benchmark for graph clustering with unique characteristics. Our graphs are at the same time heterogeneous, i.e., include different types of nodes and node attributes, and comprise overlapping clusters, i.e., a node may belong to multiple clusters. We empirically show the arduous characteristics of the datasets.

In this paper:

  • We propose GraB, a set of benchmark datasets for overlapping community detection on heterogeneous, attributed netwroks.
  • GraB combines IMDB and DBPedia in an intelligible manner.
  • We analyze the characteristics of GraB empirically.
  • We release the code and datasets for further comparisons.