Co-Membership-based Generic Anomalous Communities Detection

TL;DR

This paper introduces CMMAC, a novel, domain-free algorithm for detecting anomalous communities in networks by leveraging vertices' co-membership information, outperforming existing methods on simulated and real-world datasets.

Contribution

The paper presents CMMAC, a new generic method that uses co-membership data for anomaly detection, along with a community-structured network generator and extensive experimental validation.

Findings

CMMAC outperforms existing methods in various settings.

It effectively detects anomalies in real-world networks like Reddit and Wikipedia.

The authors provide a new dataset of labeled anomaly-infused networks.

Abstract

Nowadays, detecting anomalous communities in networks is an essential task in research, as it helps discover insights into community-structured networks. Most of the existing methods leverage either information regarding attributes of vertices or the topological structure of communities. In this study, we introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC), a novel and generic method that utilizes the information of vertices co-membership in multiple communities. CMMAC is domain-free and almost unaffected by communities' sizes and densities. Specifically, we train a classifier to predict the probability of each vertex in a community being a member of the community. We then rank the communities by the aggregated membership probabilities of each community's vertices. The lowest-ranked communities are considered to be anomalous. Furthermore, we present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities to facilitate research in the field. We utilized it to generate two datasets, composed of thousands of labeled anomaly-infused networks, and published them. We experimented extensively on thousands of simulated, and real-world networks, infused with artificial anomalies. CMMAC outperformed other existing methods in a range of settings. Additionally, we demonstrated that CMMAC can identify abnormal communities in real-world unlabeled networks in different domains, such as Reddit and Wikipedia.