Many real-world social networks such as brain graph, protein structure, food web, transportation system, World Wide Web, online social networks exist in the form of a complex network. In such complex networks, pattern identification or community detection requires extra effort in which identifying community is a significant problem in various research areas. Most of the clustering methods on graphs predominantly emphasize on the topological structure without considering connectivity between vertices and not bearing in mind the vertex properties/attributes or similarity-based on indirectly connected vertices. A novel clustering algorithm SAG-Cluster with K-medoids framework presented for detecting communities using a collaborative similarity measure which considers attribute importance in case the pair of disconnected nodes. A novel path strategy using classic Basel problem for the indirectly connected node as well as balanced attribute similarity and distance function is proposed. On two real data sets, experimental results show the effectiveness of SAG-Cluster with the comparison of other relevant methods.

许多现实世界中的社交网络（例如脑图，蛋白质结构，食物网，运输系统，万维网，在线社交网络）以复杂网络的形式存在。在这种复杂的网络中，模式识别或社区检测需要付出额外的努力，其中识别社区是各个研究领域中的重要问题。图上的大多数聚类方法主要强调拓扑结构，而不考虑顶点之间的连通性，并且不考虑基于间接连接的顶点的顶点属性/属性或相似性。提出了一种新颖的具有K-medoids框架的聚类算法SAG-Cluster，用于使用协作相似性度量来检测社区，该度量在属性对断开的情况下考虑了属性的重要性。提出了一种基于经典巴塞尔问题的间接连接节点以及平衡的属性相似度和距离函数的路径策略。在两个真实数据集上，实验结果通过与其他相关方法的比较证明了SAG-Cluster的有效性。