The discovery of community structures in social networks has gained
considerable attention as a fundamental problem for 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 data 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 steps: 1) initial network inference, 2) node-level
community-affiliation embedding based on graph neural networks (GNNs) trained
by our new reconstruction loss, and 3) network exploration via
community-affiliation-based node queries, where Steps 2 and 3 are performed
iteratively. Experimental results demonstrate that META-CODE exhibits (a)
superiority over benchmark methods for overlapping community detection, (b) the
effectiveness of our training model, and (c) fast network exploration.

META-CODE 是一种新的端到端解决方案，可通过易于收集的节点元数据和探索性学习来检测未知拓扑结构的网络中的重叠社区。其算法包括三个步骤：1）初始化网络推断，2）基于图神经网络的节点层次社区归属嵌入，3）基于社区归属的网络探索。实验结果表明，META-CODE 具有优于基准方法的性能，训练模型的有效性以及快速网络探索。