The Artificial Benchmark for Community Detection with Outliers and Overlapping Communities (ABCD+$o^2$)

TL;DR

This paper introduces ABCD+$o^2$, an advanced random graph model for community detection benchmarking that incorporates outliers and overlapping communities, offering a faster and more analytically tractable alternative to existing models.

Contribution

The paper presents a novel extension of the ABCD model to include outliers and overlapping communities, enhancing its realism and analytical utility for community detection research.

Findings

ABCD+$o^2$ enables realistic simulation of complex community structures.

The model is faster and more interpretable than existing benchmarks.

It facilitates analytical investigations of community detection algorithms.

Abstract

The Artificial Benchmark for Community Detection (ABCD) graph is a random graph model with community structure and power-law distribution for both degrees and community sizes. The model generates graphs similar to the well-known LFR model but it is faster, more interpretable, and can be investigated analytically. In this paper, we use the underlying ingredients of the ABCD model, and its generalization to include outliers (ABCD+$o$), and introduce another variant that allows for overlapping communities, ABCD+$o^2$.