A Multiobjective Genetic Algorithm to Find Communities in Complex Networks
A Multiobjective Genetic Algorithm to Find Communities in Complex Networks
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DOI：
10.1109/tevc.2011.2161090
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发表时间：
2012-06-01
影响因子：
14.3
通讯作者：
Pizzuti, Clara
中科院分区：
文献类型：
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作者：
Pizzuti, Clara
提出了一种求解复杂网络社团结构的多目标遗传算法。该算法优化了两个目标函数，能够识别密集连接的节点组具有稀疏的互连。该方法生成一组在不同层次级别的网络划分，其中在更深层次的解决方案，包括更高数量的模块，包含在具有较低数量的社区的解决方案。模块的数量由目标函数的较佳折衷值自动确定。在人工合成网络和真实的生活网络上的实验表明，该算法能够成功地检测出网络结构，与现有的方法相比具有一定的竞争力。
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.