# Co-Membership-based Generic Anomalous Communities Detection

arXiv: https://arxiv.org/abs/2203.16246 · HTML: https://mlsanity.com/paper/2203.16246/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 76 references that are in the map.
- First posted on arXiv 30 Mar 2022.

## Builds on (most cited first, 13 of 13)

- [XGBoost: A Scalable Tree Boosting System](https://mlsanity.com/paper/1603.02754/) (2016)
- [node2vec: Scalable Feature Learning for Networks](https://mlsanity.com/paper/1607.00653/) (2016)
- [Variational Graph Auto-Encoders](https://mlsanity.com/paper/1611.07308/) (2016)
- [Graph-based Anomaly Detection and Description: A Survey](https://mlsanity.com/paper/1404.4679/) (2014)
- [Adaptive Graph Encoder for Attributed Graph Embedding](https://mlsanity.com/paper/2007.01594/) (2020)
- [An Army of Me: Sockpuppets in Online Discussion Communities](https://mlsanity.com/paper/1703.07355/) (2017)
- [Multi-scale Anomaly Detection on Attributed Networks](https://mlsanity.com/paper/1912.04144/) (2019)
- [GLAD: Group Anomaly Detection in Social Media Analysis- Extended Abstract](https://mlsanity.com/paper/1410.1940/) (2014)
- [Equation Parsing: Mapping Sentences to Grounded Equations](https://mlsanity.com/paper/1609.08824/) (2016)
- [Smoke Screener or Straight Shooter: Detecting Elite Sybil Attacks in User-Review Social Networks](https://mlsanity.com/paper/1709.06916/) (2017)
- [A Spectral Framework for Anomalous Subgraph Detection](https://mlsanity.com/paper/1401.7702/) (2014)
- [Generic Anomalous Vertices Detection Utilizing a Link Prediction Algorithm](https://mlsanity.com/paper/1610.07525/) (2016)
- [The Rise and Fall of Network Stars: Analyzing 2.5 million graphs to reveal how high-degree vertices emerge over time](https://mlsanity.com/paper/1706.06690/) (2017)

## Abstract (arXiv)

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.