GraB: Graph Benchmark for Heterogeneous Graph Clustering

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

Keywords: Graph clustering, heterogeneous graph, benchmark, attributed graphs, overlapping clusters

TL;DR: A novel benchmark for graph clustering with heterogeneous attributed graphs and overlapping clusters.

Abstract: 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; GraB is available at https://github.com/AU-DIS/GraB.

Type Of Submission: Extended abstract (max 4 main pages).

Type Of Submission: Extended abstract.