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A Two-Dimensional Genetic Algorithm for Identifying Overlapping Communities in Dynamic Networks

机译：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：对社区及其在动态网络中的演化的分析是一项具有广泛应用前景的具有挑战性的研究。最近的研究发现，在某些实际网络中，社区之间的重叠比非重叠部分更紧密地连接在一起。研究结果与重叠社区的当前概念不同。现有方法可能无法检测到此类社区。在本文中，我们首先将发现扩展到分析动态网络，并开发一种有效的算法，以使用进化聚类在统一的过程中检测密集的重叠社区及其演化。我们还介绍了具有多维染色体的遗传算法，以描述我们框架中属于多个社区的节点。实验研究表明，与动态网络中的最新方法相比，我们的方法可以成功捕获密集的重叠并更准确地识别相关社区。

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