论文标题
元代码：通过拓扑未知网络中的探索性学习通过探索性学习
META-CODE: Community Detection via Exploratory Learning in Topologically Unknown Networks
论文作者
论文摘要
社交网络中社区结构的发现已引起了人们的关注，这是各种网络分析任务的基本问题。但是，由于隐私问题或访问限制，网络结构通常是未知的，从而使既定的社区检测方法无效而没有昂贵的数据获取。为了应对这一挑战，我们提出了元代码，这是一种新型的端到端解决方案，用于通过易于收集的节点元数据在探索性学习的帮助下检测具有未知拓扑的网络中的重叠社区。具体而言，元代码由三个步骤组成：1）初始网络推断，2）基于图形神经网络（GNNS）的节点级别的社区 - 附加嵌入，该嵌入受我们的新重建损失培训的图形神经网络（GNNS），以及3）通过基于社区接用的节点Queries进行的网络探索，其中步骤2和3进行了迭代进行。实验结果表明，元代码表现出（a）比基准方法优于重叠社区检测的优势，（b）我们训练模型的有效性以及（c）快速网络探索。
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.
