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Related Experiment Video

Updated: Aug 14, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

Co-Membership-based Generic Anomalous Communities Detection.

Shay Lapid¹, Dima Kagan¹, Michael Fire¹

¹Software and Information Systems Engineering, Ben-Gurion University of the Negev, Beer-Sheva, Israel.

Neural Processing Letters

|January 10, 2023

Summary

This summary is machine-generated.

Detecting anomalous communities in networks is crucial. The Co-Membership-based Generic Anomalous Communities Detection Algorithm (CMMAC) effectively identifies these anomalies by analyzing vertex co-membership, outperforming existing methods.

Keywords:

Anomalous community detection Anomalous subgraph detection Anomaly detection Complex networks analysis Social networks analysis

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Last Updated: Aug 14, 2025

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

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Published on: September 25, 2021

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Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Quantification of Protein Interaction Network Dynamics using Multiplexed Co-Immunoprecipitation

Published on: August 21, 2019

Area of Science:

Network Science
Data Mining
Machine Learning

Background:

Detecting anomalous communities in networks is vital for uncovering network insights.
Existing methods often rely on vertex attributes or community topology.
A gap exists in methods utilizing vertex co-membership across multiple communities.

Purpose of the Study:

Introduce a novel, generic algorithm for anomalous community detection.
Develop a method robust to community size, density, and domain.
Provide a tool for generating labeled anomaly-infused networks for research.

Main Methods:

Propose the Co-Membership-based Generic Anomalous Communities Detection Algorithm (CMMAC).
Utilize vertex co-membership information across multiple communities.
Train a classifier to predict vertex community membership probability and rank communities.

Main Results:

CMMAC demonstrates superior performance over existing methods on simulated and real-world networks.
The algorithm is domain-free and resilient to variations in community size and density.
Successfully identified abnormal communities in unlabeled real-world networks (e.g., Reddit, Wikipedia).

Conclusions:

CMMAC offers a powerful and versatile approach for anomalous community detection.
The developed network generation algorithm facilitates reproducible research in this field.
CMMAC shows practical applicability across diverse network domains.