Community detection in networks by using multiobjective evolutionary algorithm with decomposition
Community detection in networks by using multiobjective evolutionary algorithm with decomposition
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DOI：
10.1016/j.physa.2012.03.021
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发表时间：
2012-08-01
影响因子：
3.3
通讯作者：
Jiao, Licheng
中科院分区：
文献类型：
--
作者：
Gong, Maoguo;Ma, Lijia;Jiao, Licheng
群落结构是复杂网络的一个重要特征。大多数基于优化的社区检测算法采用单一优化标准。本研究采用基于分解的多目标进化算法，将社区检测作为一个多目标优化问题来解决。该算法使内部度密度最大化，同时使外部度密度最小化。它可以产生一组解决方案，这些解决方案可以表示不同层次的网络的各种划分。社区的数量由我们的算法产生的非支配个体自动决定。在合成和真实网络数据集上的实验验证了我们的算法在发现高质量社区结构方面是高效的。(C) 2012 Elsevier B.V.版权所有
Community structure is an important property of complex networks. Most optimization-based community detection algorithms employ single optimization criteria. In this study, the community detection is solved as a multiobjective optimization problem by using the multiobjective evolutionary algorithm based on decomposition. The proposed algorithm maximizes the density of internal degrees, and minimizes the density of external degrees simultaneously. It can produce a set of solutions which can represent various divisions to the networks at different hierarchical levels. The number of communities is automatically determined by the non-dominated individuals resulting from our algorithm. Experiments on both synthetic and real-world network datasets verify that our algorithm is highly efficient at discovering quality community structure. (C) 2012 Elsevier B.V. All rights reserved.