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首页> 外文会议>IEEE International Conference on Tools with Artificial Intelligence >A Two-Dimensional Genetic Algorithm for Identifying Overlapping Communities in Dynamic Networks
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A Two-Dimensional Genetic Algorithm for Identifying Overlapping Communities in Dynamic Networks

机译：一种识别动态网络中重叠社区的二维遗传算法

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摘要

The analysis of communities and their evolution in dynamic networks is a challenging research with broad applications. The recent studies have found that the overlaps between communities are more densely connected than the non-overlapping parts in some real networks. The findings are different from the present concepts of the overlapping communities. Existing methods may fail to detect this kind of communities. In this paper, we first extend the findings to analyze dynamic networks and develop an effective algorithm for detecting dense overlapping communities and their evolution in a unified process by using evolutionary clustering. We also introduce genetic algorithm with multidimensional chromosome to describe nodes belonging to multiple communities in our framework. The experimental studies demonstrate that our method successfully captures dense overlaps and identifies relevant communities more accurately than the state-of-the-art methods in dynamic networks.
机译：对动态网络的社区分析及其演化是具有广泛应用的具有挑战性的研究。最近的研究发现，社区之间的重叠比某些真实网络中的非重叠部分更密集地连接。该发现与重叠社区的目前的概念不同。现有方法可能无法检测到这种社区。在本文中，我们首先通过使用进化聚类，扩展了分析动态网络的调查结果，并开发了一种用于检测统一过程中的密集重叠社区及其演化的有效算法。我们还引入了具有多维染色体的遗传算法，以描述框架中属于多个社区的节点。实验研究表明，我们的方法成功地捕获密集重叠并比动态网络中的最先进的方法更准确地识别相关社区。

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