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 unknown, thereby rendering established community detection
approaches ineffective without costly network topology acquisition. To tackle
this challenge, we present META-CODE, a novel end-to-end solution for detecting
overlapping communities in networks with unknown topology via exploratory
learning aided by easy-to-collect node metadata. 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 comprehensive evaluations using five real-world datasets, we
demonstrate that META-CODE exhibits (a) its superiority over benchmark
community detection methods, (b) empirical evaluations as well as theoretical
findings to see the effectiveness of our node query, (c) the influence of each
module, and (d) its computational efficiency.

通过采集易于获得的节点元数据，META-CODE 提出了一种新的端到端解决方案，用于检测未知拓扑结构的网络中的重叠社区， META-CODE 通过图神经网络训练节点层次社区从属嵌入，并使用 Siamese 神经网络模型进行网络推理。