IEEE Trans Cybern. 2020 Dec;50(12):4997-5009. doi: 10.1109/TCYB.2018.2889413. Epub 2020 Dec 3.
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.
方法检测社区结构的复杂网络主要集中在网络拓扑,忽略了丰富的内容信息通常与节点。在过去的几年中,组成维度包含在许多真实网络已经被认为是发现网络划分更好地反映组织群体的基本。在本文中,我们提出了一种多目标遗传框架,该框架集成了拓扑和组成维度来揭示属性网络中的社区结构。这种方法允许不同的结构措施来搜索密集连接的社区，以及属性之间的相似性度量来获得高的同社区特征同质性。一个有效的和有效的后处理局部合并过程能够生成高质量的解决方案,实验结果证实了这一点在合成和真实世界的网络,和与几个最先进的方法比较。
