  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 often uncertain, thereby rendering established community detection
approaches ineffective without costly network topology acquisition. To tackle
this challenge, we present META-CODE, a unified framework for detecting
overlapping communities via exploratory learning aided by easy-to-collect node
metadata when networks are topologically unknown (or only partially known).
Specifically, META-CODE consists of three iterative steps in addition to the
initial network inference step: 1) node-level community-affiliation embeddings
based 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 model
from the explored network. Through extensive experiments on five real-world
datasets including two large networks, we demonstrated: (a) the superiority of
META-CODE over benchmark community detection methods, achieving remarkable
gains up to 151.27% compared to the best existing competitor, (b) the impact of
each module in META-CODE, (c) the effectiveness of node queries in META-CODE
based on empirical evaluations and theoretical findings, (d) the convergence of
the inferred network, and (e) the computational efficiency of META-CODE.
