Joint community and anomaly tracking in dynamic networks

TL;DR

This paper introduces a joint method for tracking communities and detecting anomalies in dynamic networks, improving understanding of network behavior and identifying malicious or fake nodes.

Contribution

It proposes a novel dynamic factor model that captures community memberships and anomalies simultaneously, with efficient algorithms for online and decentralized processing.

Findings

Successfully detects communities and anomalies in synthetic networks.

Effectively uncovers underlying communities and anomalous nodes in real network data.

Provides a scalable approach suitable for real-time applications.

Abstract

Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection finds many practical applications in diverse settings. Communities in online social networks are indicative of shared functional roles, or affiliation to a common socio-economic status, the knowledge of which is vital for targeted advertisement. In buyer-seller networks, community detection facilitates better product recommendations. Unfortunately, reliability of community assignments is hindered by anomalous user behavior often observed as unfair self-promotion, or "fake" highly-connected accounts created to promote fraud. The present paper advocates a novel approach for jointly tracking communities while detecting such anomalous nodes in time-varying networks. By postulating edge creation as the result of mutual community participation by node pairs, a dynamic factor model with anomalous memberships captured through a sparse outlier matrix is put forth. Efficient tracking algorithms suitable for both online and decentralized operation are developed. Experiments conducted on both synthetic and real network time series successfully unveil underlying communities and anomalous nodes.