Zhang Zhong-Yuan, Sun Kai-Di, Wang Si-Qi
School of Statistics and Mathematics, Central University of Finance and Economics, P.R.China.
Sci Rep. 2013 Nov 19;3:3241. doi: 10.1038/srep03241.
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.
复杂网络中的社区结构检测非常重要，因为它有助于更好地理解网络拓扑以及网络的运行方式。然而，社区结构仍然没有一个清晰且被广泛接受的定义，并且在实践中，不同的模型可能会给出截然不同的社区检测结果，这使得结果难以解释。在本文中，与传统方法不同，我们设计了一种用于社区检测的增强型半监督学习框架，该框架可以有效地整合可用的先验信息来指导检测过程，并使结果更具可解释性。通过逻辑推理，先验信息得到了更充分的利用。在合成网络和真实世界网络上的实验证实了该框架的有效性。
