基于稀疏子空间聚类的多层网络社团检测

现有的子空间聚类方法大多只适用于单层网络，或者仅对多层网络中每层的聚类结果简单地进行平均，未考虑每层网络中包含信息量不同的特点，致使聚类性能受限。针对该问题，提出一种面向多层网络的稀疏子空间聚类方法。将距离正则项和非负约束条件集成到稀疏子空间聚类框架中，从而在聚类时能够同时利用数据的全局信息和局部信息进行图学习。此外，通过引入稀疏约束使学习到的图具有更清晰的聚类结构，并设计迭代算法进行优化求解。在多个真实数据集上的实验结果表明，该方法能够挖掘网络不同层的互补信息，得到准确的一致性联合稀疏表示，有效提高社团聚类性能。

Multi-Layer Network Community Detection Based on Sparse Subspace Clustering

The existing subspace clustering methods are only applicable to single-layer networks, or just average the clustering results of each layer in the multi-layer network.They fail to consider the different amounts of information contained in each layer, which causes a reduction in the subspace clustering performance.To address the problem, a sparse subspace clustering method for multi-layer networks is proposed.Firstly, a distance regularization term and a non-negative constraint are jointly integrated into the framework of sparse subspace clustering, which enables the method to simultaneously exploit the global and local information of data for graph learning during clustering.In addition, the sparse constraint is introduced to provide the learned graph with a clear clustering structure, and an iterative algorithm is designed to optimize the solution.Experimental results on multiple real datasets show that the proposed method can mine the complementary information of different layers of the network, obtain an accurate consistent joint sparse representation, and effectively improve the community clustering performance of multi-layer networks.