Co-Membership-based Generic Anomalous Communities Detection
- URL: http://arxiv.org/abs/2203.16246v1
- Date: Wed, 30 Mar 2022 12:23:55 GMT
- Title: Co-Membership-based Generic Anomalous Communities Detection
- Authors: Shay Lapid, Dima Kagan, Michael Fire
- Abstract summary: We introduce the Co-Membership-based Generic Anomalous Communities Detection Algorithm (referred as to CMMAC)
CMMAC is domain-free and almost unaffected by communities' sizes and densities.
We present an algorithm for generating a community-structured random network enabling the infusion of anomalous communities.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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