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In social networks, the discovery of community structures has receivedconsiderable attention as a fundamental problem in various network analysistasks. However, due to privacy concerns or access restrictions, the networkstructure is often uncertain, thereby rendering established community detectionapproaches ineffective without costly network topology acquisition. To tacklethis challenge, we present META-CODE, a unified framework for detectingoverlapping communities via exploratory learning aided by easy-to-collect nodemetadata when networks are topologically unknown (or only partially known).Specifically, META-CODE consists of three iterative steps in addition to theinitial network inference step: 1) node-level community-affiliation embeddingsbased on graph neural networks (GNNs) trained by our new reconstruction loss,2) network exploration via community-affiliation-based node queries, and 3)network inference using an edge connectivity-based Siamese neural network modelfrom the explored network. Through extensive experiments on three real-worlddatasets including two large networks, we demonstrate: (a) the superiority ofMETA-CODE over benchmark community detection methods, achieving remarkablegains up to 65.55% on the Facebook dataset over the best competitor among ourselected competitive methods in terms of normalized mutual information (NMI),(b) the impact of each module in META-CODE, (c) the effectiveness of nodequeries in META-CODE based on empirical evaluations and theoretical findings,and (d) the convergence of the inferred network.

A Unified Framework for Exploratory Learning-Aided Community Detection Under Topological Uncertainty