基于图聚类与蚁群算法的社交网络聚类算法

针对社交网络中社交关系的有向性与多样性，提出了一种基于图聚类与蚁群算法的社交网络聚类算法。首先，在网络覆盖率的约束下为社交网络建立有向、非全连接的二维图模型；然后，采用K-medoids算法搜索用户分组的中心用户，采用人工蚁群算法在2D图中搜索各个用户与中心用户的相似性，将满足相似性阈值的用户分为同一个用户组。设计了低活跃用户的预测机制解决网络的稀疏性问题与冷启动问题。此外，通过网络覆盖率的约束条件权衡聚类准确率与覆盖率两个指标。仿真实验结果表明，该算法实现了较好的社交网络聚类性能，并且有效地缓解了稀疏性问题与冷启动问题。

Clustering algorithm of social networks based on graph clustering and ant colony optimization algorithm

Aiming at the properties of direction and diversity of social relationships in the social networks, this paper proposed a clustering algorithm of social networks based on graph clustering and ant colony optimization algorithm. Firstly, it constructed a directed and non fully connected complete graph for the social networks under constraint condition of network coverage; then, it adopted K-medoids algorithm to search the center users of all user groups, and it adopted ant colony optimization to search the similarities of each user and center users in the graph, it grouped the users satisfied the threshold condition into the same group. This paper also designed a prediction mechanism of low active degree users to resolve the sparsity problem and cold-start problem, besides, the network coverage constraint condition was set to balance the indexes of accuracy and coverage. Simulation experimental results indicate that the proposed algorithm realizes a good clustering performance of social networks, and it reduces the problems of sparsity and cold-start effectively.