Abstract: Real-world multiplex networks often have the characteristics of multi-dimensional and high complexity. The clustering accuracy of existing approaches that only use network topology information for clustering often cannot be guaranteed. To address the problem， the paper proposes a semi-supervised model with non-negative matrix factorization （SeNMF）. Firstly， the model designs a greedy search method based on the PageRank algorithm to obtain the consensus prior information of network. The prior information is used to enhance the topology of each network layer to reduce network noise. Then， the overall non-negative matrix factorization is used to obtain a better common low-dimensional representation matrix by fusing the low-dimensional representations of all network layers on the Grassmannian manifold. Finally， K-means is used to obtain the public community structure of the network. Extensive experiments show that SeNMF achieves the outstanding performance over the state-of-the-art approaches， whether it is the increase of network layers or the enhancement of inter-layer noise.

Key words: multiplex network clustering, non-negative matrix factorization, semi-supervised model, consensus prior information, public community structure