We propose a simple mixed membership model for social network clustering in this paper. A flexible function is adopted to measure affinities among a set of entities in a social network. The model not only allows each entity in the network to possess more than one membership, but also provides accurate statistical inference about network structure. We estimate the membership parameters using an MCMC algorithm. We evaluate the performance of the proposed algorithm by applying our model to two empirical social network data, the Zachary club data and the bottlenose dolphin network data. We also conduct some numerical studies based on synthetic networks for further assessing the effectiveness of our algorithm. In the end, some concluding remarks and future work are addressed briefly.

翻译：本文提出了一种用于社交网络聚类的简单混合隶属度模型。采用灵活的函数来度量社交网络中实体间的亲密度。该模型不仅允许网络中的每个实体拥有多个隶属关系，还能对网络结构进行精确的统计推断。我们使用MCMC算法估计隶属度参数。通过将模型应用于两个经验社交网络数据集——扎卡里俱乐部数据和宽吻海豚网络数据，评估了所提算法的性能。此外，基于合成网络进行了数值研究，以进一步验证算法的有效性。最后，简要总结了相关结论并展望了未来工作方向。