IEEE Trans Cybern. 2021 Jan;51(1):138-150. doi: 10.1109/TCYB.2019.2931983. Epub 2020 Dec 22.
In many real-world networks, the structural connections of networks and the attributes about each node are always available. We typically call such graphs attributed networks, in which attributes always play the same important role in community detection as the topological structure. It is shown that the very existence of overlapping communities is one of the most important characteristics of various complex networks, while the majority of the existing community detection methods was designed for detecting separated communities in attributed networks. Therefore, it is quite challenging to detect meaningful overlapping structures with the combination of node attributes and topological structures. Therefore, in this article, we propose a multiobjective evolutionary algorithm based on the similarity attribute for overlapping community detection in attributed networks (MOEA-SA ). In MOEA-SA , a modified extended modularity EQ , dealing with both directed and undirected networks, is well designed as the first objective. Another objective employed is the attribute similarity S . Then, a novel encoding and decoding strategy is designed to realize the goal of representing overlapping communities efficiently. MOEA-SA runs under the framework of the nondominated sorting genetic algorithm II (NSGA-II) and can automatically determine the number of communities. In the experiments, the performance of MOEA-SA is validated on both synthetic and real-world networks, and the experimental results demonstrate that our method can effectively find Pareto fronts about overlapping community structures with practical significance in both directed and undirected attributed networks.
在许多真实世界的网络中，网络的结构连接和每个节点的属性都是可用的。我们通常将这样的图称为属性网络，其中属性在社区检测中与拓扑结构一样起着重要作用。事实证明，重叠社区的存在是各种复杂网络的最重要特征之一，而大多数现有的社区检测方法是为检测属性网络中的分离社区而设计的。因此，结合节点属性和拓扑结构来检测有意义的重叠结构具有一定的挑战性。因此，在本文中，我们提出了一种基于属性相似性的多目标进化算法，用于属性网络中的重叠社区检测（MOEA-SA）。在 MOEA-SA 中，我们设计了一个改进的扩展模块化 EQ，该 EQ 可以处理有向和无向网络，作为第一个目标。另一个目标是属性相似性 S。然后，我们设计了一种新颖的编码和解码策略，以有效地实现表示重叠社区的目标。MOEA-SA 在非支配排序遗传算法 II（NSGA-II）的框架下运行，可以自动确定社区的数量。在实验中，我们在合成和真实网络上验证了 MOEA-SA 的性能，实验结果表明，我们的方法可以有效地在有向和无向属性网络中找到具有实际意义的重叠社区结构的 Pareto 前沿。
