关键词: 异质网络, 社区发现, 非负矩阵分解, 图正则化

Abstract:

Mining valuable and stable communities in data networks is of great value for the acquisition, recommendation and evolution of network information. Aiming at the problem that the existing heterogeneous network clustering method is difficult to effectively integrate heterogeneous information in the network in the same dimension, this paper proposes a heterogeneous network clustering method based on graph regularization non-negative matrix factorization. By adding the graph regularization term, the internal connection relationship between the central type subspace and the attribute type subspace is introduced as a constraint item into the non-negative matrix decomposition model, thereby finding the compact embedding of high dimensional data in low dimensional space, which successfully eliminates partial noise between heterogeneous nodes. At the same time, it optimizes the consensus matrix reflecting the common structure of different sub-networks, effectively integrates heterogeneous information, and preserves the integrity of heterogeneous information to a large extent during the dimension reduction process. The accuracy of the heterogeneous network clustering method is improved, and the experimental results on the real world dataset also verify the effectiveness of the method.

Key words: heterogeneous network, community detecting, non-negative matrix factorization, graphregular term