Zheng Wei, Sun Jianyong, Zhang Qingfu, Xu Zongben
IEEE Trans Cybern. 2023 Sep;53(9):5469-5482. doi: 10.1109/TCYB.2022.3155646. Epub 2023 Aug 17.
Detecting overlapping communities of an attribute network is a ubiquitous yet very difficult task, which can be modeled as a discrete optimization problem. Besides the topological structure of the network, node attributes and node overlapping aggravate the difficulty of community detection significantly. In this article, we propose a novel continuous encoding method to convert the discrete-natured detection problem to a continuous one by associating each edge and node attribute in the network with a continuous variable. Based on the encoding, we propose to solve the converted continuous problem by a multiobjective evolutionary algorithm (MOEA) based on decomposition. To find the overlapping nodes, a heuristic based on double-decoding is proposed, which is only with linear complexity. Furthermore, a postprocess community merging method in consideration of node attributes is developed to enhance the homogeneity of nodes in the detected communities. Various synthetic and real-world networks are used to verify the effectiveness of the proposed approach. The experimental results show that the proposed approach performs significantly better than a variety of evolutionary and nonevolutionary methods on most of the benchmark networks.
检测属性网络中的重叠社区是一项普遍存在但非常困难的任务，它可以被建模为一个离散优化问题。除了网络的拓扑结构外，节点属性和节点重叠显著加剧了社区检测的难度。在本文中，我们提出了一种新颖的连续编码方法，通过将网络中的每条边和节点属性与一个连续变量相关联，将具有离散性质的检测问题转化为一个连续问题。基于这种编码，我们提出通过基于分解的多目标进化算法（MOEA）来解决转化后的连续问题。为了找到重叠节点，提出了一种基于双重解码的启发式方法，其复杂度仅为线性。此外，还开发了一种考虑节点属性的后处理社区合并方法，以增强检测到的社区中节点的同质性。使用各种合成网络和真实网络来验证所提方法的有效性。实验结果表明，在所使用的大多数基准网络上，所提方法的性能明显优于多种进化方法和非进化方法。
