A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty
Year: 2025
Type: article
Abstract: In social networks, the discovery of community structures has received considerable attention as a fundamental problem in various network analysis tasks. However, due to privacy concerns or access restrictions, the network structure is oftenuncertain, thereby rendering established community detection approaches ineffective without costly network topology acquisition. To tackle this challenge, we presentMETA-CODE, a unified framework for detecting overlapping communities viaexploratory learningaided byeasy-to-collectnode metadata when networks are topologically unknown (or only partially known). Specifically,META-CODEconsists of three iterative steps in addition to the initial network inference step: 1) node-levelcommunity-affiliation embeddingsbased on graph neural networks (GNNs) trained by our new reconstruction loss, 2)network explorationvia community-affiliation-based node queries, and 3)network inferenceusing an edge connectivity-based Siamese neural network model from the explored network. Through extensive experiments on three real-world datasets including two large networks, we demonstrate: (a) the superiority ofMETA-CODEover benchmark community detection methods, achieving remarkable gains up to 65.55% on the Facebook dataset over the best competitor among our selected competitive methods in terms of normalized mutual information (NMI), (b) the impact of each module inMETA-CODE, (c) the effectiveness of node queries inMETA-CODEbased on empirical evaluations and theoretical findings, and (d) the convergence of the inferred network.
Authors: Yu Hou, Cong Tran, Ming Li, Won-Yong Shin
Language: English
Subfield: Statistical and Nonlinear Physics
Field: Physics and Astronomy
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4409102257