Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities, in this paper, a joint optimization framework, Communities detected on Predicted Edges, is proposed. Our goal to improve the quality of community detection through learning the probability of unseen links and the probability of community affiliation of nodes simultaneously. Link prediction and community detection are mutually reinforced to generate better results of both tasks. Experiments conducted on a number of well-known network data show that the proposed COPE stably outperforms several state-of-the-art community detection algorithms.

由于诸如图采样和隐私问题之类的原因，现实世界的网络数据可能不完整（例如，部分观察到社交联系）。因此，基于这种不完整的网络信息检测到的社区可能不如基于完全观察到的网络识别出的社区可靠。现有研究首先预测缺失的链接，然后检测社区，但在本文中，这是一个联合优化框架，即在预测边缘检测到社区，建议。我们的目标是通过同时学习看不见的链接的概率和节点的社区隶属关系的概率来提高社区检测的质量。链接预测和社区检测相互加强，以生成两个任务的更好结果。在许多知名网络数据上进行的实验表明，提出的COPE稳定地胜过了几种最新的社区检测算法。