Identification of Overlapping Communities via Constrained Egonet Tensor Decomposition

TL;DR

This paper introduces a novel tensor-based method for detecting overlapping communities in networks by leveraging egonet structures and constrained PARAFAC decomposition, improving robustness and handling dynamic graphs.

Contribution

It proposes a new sparse tensor representation of egonets and a constrained tensor approximation framework for overlapping community detection, including dynamic networks.

Findings

Tensor representation captures multi-hop connectivity patterns.

Method outperforms traditional approaches on synthetic and real-world networks.

Handles overlapping and evolving communities effectively.

Abstract

Detection of overlapping communities in real-world networks is a generally challenging task. Upon recognizing that a network is in fact the union of its egonets, a novel network representation using multi-way data structures is advocated in this contribution. The introduced sparse tensor-based representation exhibits richer structure compared to its matrix counterpart, and thus enables a more robust approach to community detection. To leverage this structure, a constrained tensor approximation framework is introduced using PARAFAC decomposition. The arising constrained trilinear optimization is handled via alternating minimization, where intermediate subproblems are solved using the alternating direction method of multipliers (ADMM) to ensure convergence. The factors obtained provide soft community memberships, which can further be exploited for crisp, and possibly-overlapping community assignments. The framework is further broadened to include time-varying graphs, where the edgeset as well as the underlying communities evolve through time. Performance of the proposed approach is assessed via tests on benchmark synthetic graphs as well as real-world networks. As corroborated by numerical tests, the proposed tensor-based representation captures multi-hop nodal connections, that is, connectivity patterns within single-hop neighbors, whose exploitation yields a more robust community identification in the presence of mixing as well as overlapping communities.