A Multiobjective Genetic Algorithm to Find Communities in Complex Networks
提出一种多目标遗传算法，通过优化两个目标函数来识别复杂网络中连接紧密的节点群组，自动确定社区数量，在合成和真实网络上表现优于现有方法。
A multiobjective genetic algorithm to uncover community structure in complex network is proposed. The algorithm optimizes two objective functions able to identify densely connected groups of nodes having sparse inter-connections. The method generates a set of network divisions at different hierarchical levels in which solutions at deeper levels, consisting of a higher number of modules, are contained in solutions having a lower number of communities. The number of modules is automatically determined by the better tradeoff values of the objective functions. Experiments on synthetic and real life networks show that the algorithm successfully detects the network structure and it is competitive with state-of-the-art approaches.