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.

翻译：社会网络中社区结构的发现作为各种网络分析任务的一个根本问题,引起了相当的重视,然而,由于隐私问题或准入限制,网络结构往往不为人所知,因此,在没有昂贵的数据获取的情况下,既定的社区检测方法无效。为了应对这一挑战,我们提出了新的端对端解决方案META-CODE,这是通过易于收集的节点元数据辅助的探索性学习,在具有未知地形的网络中发现重叠社区的新的端对端解决方案。具体地说,META-CODE由三个步骤组成:1) 初步网络推断,2) 通过我们新的重建损失所培训的图形神经网络(GNNN)嵌入节点,3) 通过基于社区情感的节点查询进行网络探索,步骤2和步骤3是迭接的。实验结果表明,META-CODE展示了(a) 超越重叠社区检测基准方法的优势,(b) 我们的培训模式的有效性,以及(c) 快速网络探索。