Enhanced community structure detection in complex networks with partial background information.
Enhanced community structure detection in complex networks with partial background information.
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DOI：
10.1038/srep03241
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发表时间：
2013-11-19
影响因子：
4.6
通讯作者：
Wang SQ
中科院分区：
文献类型：
--
作者：
Zhang ZY;Sun KD;Wang SQ
复杂网络中的社团结构检测是非常重要的，因为它可以帮助我们更好地理解网络的拓扑结构和网络的工作方式。然而，目前还没有一个明确的和广泛接受的社区结构的定义，在实践中，不同的模型可能会给出非常不同的社区的结果，使其难以解释的结果。与传统方法不同，本文设计了一种增强型半监督学习社区检测框架，该框架能够有效地结合已有的先验信息指导社区检测过程，并使检测结果更具可解释性。通过逻辑推理，更充分地利用了先验信息。在合成网络和真实网络上的实验证实了该框架的有效性。
Community structure detection in complex networks is important since it can help better understand the network topology and how the network works. However, there is still not a clear and widely-accepted definition of community structure, and in practice, different models may give very different results of communities, making it hard to explain the results. In this paper, different from the traditional methodologies, we design an enhanced semi-supervised learning framework for community detection, which can effectively incorporate the available prior information to guide the detection process and can make the results more explainable. By logical inference, the prior information is more fully utilized. The experiments on both the synthetic and the real-world networks confirm the effectiveness of the framework.