Methods for detecting the community structure in complex networks have mainly focused on network topology, neglecting the rich content information often associated with nodes. In the last few years, the compositional dimension contained in many real-world networks has been recognized fundamental to find network divisions which better reflect group organization. In this paper, we propose a multiobjective genetic framework which integrates the topological and compositional dimensions to uncover community structure in attributed networks. The approach allows to experiment different structural measures to search for densely connected communities, and similarity measures between attributes to obtain high intracommunity feature homogeneity. An efficient and efficacious post-processing local merge procedure enables the generation of high quality solutions, as confirmed by the experimental results on both synthetic and real-world networks, and the comparison with several state-of-the-art methods.

中文翻译：

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聚类属性图的多目标优化和局部合并

在复杂网络中检测社区结构的方法主要集中在网络拓扑上，而忽略了通常与节点相关联的丰富内容信息。在过去的几年中，许多现实世界网络中包含的组成维度已被认为是找到更好地反映团体组织的网络部门的基础。在本文中，我们提出了一个多目标遗传框架，该框架整合了拓扑和组成维度以揭示属性网络中的社区结构。该方法允许实验不同的结构度量以搜索密集连接的社区，以及属性之间的相似性度量以获得较高的社区内部特征同质性。