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 unified framework 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 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.

翻译：在社交网络中，社区结构的发现作为各类网络分析任务中的基本问题受到了广泛关注。然而，由于隐私顾虑或访问限制，网络结构往往未知，这使得现有社区检测方法因需要昂贵的网络拓扑获取而失效。针对这一挑战，我们提出了META-CODE——一个通过易于收集的节点元数据辅助的探索性学习，在未知拓扑网络中检测重叠社区的统一框架。具体而言，除初始网络推断步骤外，META-CODE包含三个迭代步骤：1）基于图神经网络（GNN）并采用我们新提出的重建损失训练的节点级社区隶属嵌入，2）通过基于社区隶属的节点查询进行网络探索，3）利用基于边连接性的孪生神经网络模型对探索所得网络进行推断。通过在包括两个大型网络在内的五个真实数据集上的广泛实验，我们证明了：（a）META-CODE相较于基准社区检测方法具有优越性，相比最佳现有竞争者实现了高达151.27%的显著提升，（b）META-CODE中每个模块的作用，（c）基于经验评估与理论发现验证了META-CODE中节点查询的有效性，（d）推断网络的收敛性，以及（e）META-CODE的计算效率。