IEEE Trans Cybern. 2015 Nov;45(11):2585-98. doi: 10.1109/TCYB.2014.2377154. Epub 2014 Dec 18.
Community structure is one of the most important properties of complex networks and is a foundational concept in exploring and understanding networks. In real world, topology information alone is often inadequate to accurately find community structure due to its sparsity and noises. However, potential useful prior information can be obtained from domain knowledge in many applications. Thus, how to improve the community detection performance by combining network topology with prior information becomes an interesting and challenging problem. Previous efforts on utilizing such priors are either dedicated or insufficient. In this paper, we firstly present a unified interpretation to a group of existing community detection methods. And then based on this interpretation, we propose a unified semi-supervised framework to integrate network topology with prior information for community detection. If the prior information indicates that some nodes belong to the same community, we encode it by adding a graph regularization term to penalize the latent space dissimilarity of these nodes. This framework can be applied to many widely-used matrix-based community detection methods satisfying our interpretation, such as nonnegative matrix factorization, spectral clustering, and their variants. Extensive experiments on both synthetic and real networks show that the proposed framework significantly improves the accuracy of community detection, especially on networks with unclear structures.
社区结构是复杂网络最重要的性质之一，也是探索和理解网络的基础概念。在现实世界中，由于拓扑结构的稀疏性和噪声，仅拓扑信息往往不足以准确地找到社区结构。然而，在许多应用中，可以从领域知识中获得潜在有用的先验信息。因此，如何通过将网络拓扑结构与先验信息相结合来提高社区检测性能成为一个有趣且具有挑战性的问题。以前利用这些先验信息的努力要么是专门的，要么是不充分的。本文首先对一组现有的社区检测方法进行了统一的解释。然后，基于这个解释，我们提出了一个统一的半监督框架，将网络拓扑结构与先验信息结合起来进行社区检测。如果先验信息表明某些节点属于同一社区，我们通过添加图正则化项来编码它，以惩罚这些节点的潜在空间差异。这个框架可以应用于许多满足我们解释的广泛使用的基于矩阵的社区检测方法，如非负矩阵分解、谱聚类及其变体。在合成和真实网络上的大量实验表明，所提出的框架显著提高了社区检测的准确性，特别是在结构不清晰的网络上。
