Most of the existing directed graph clustering algorithms are based on the assumption of approximate linear relationship between nodes in vector space， ignoring the existence of non-linear correlation between nodes. To address this problem， a directed graph clustering algorithm based on Kernel Nonnegative Matrix Factorization （KNMF） was proposed. First， the adjacency matrix of a directed graph was projected to the kernel space by using a kernel learning method， and the node similarity in both the original and kernel spaces was constrained by a specific regularization term. Second， the objective function of graph regularization kernel asymmetric NMF algorithm was proposed， and a clustering algorithm was derived by gradient descent method under the non-negative constraints. The algorithm accurately reveals the potential structural information in the directed graph by modeling the non-linear relationship between nodes using kernel learning method， as well as considering the directivity of the links of nodes. Finally， experimental results on the Patent Citation Network （PCN） dataset show that compared with the comparison algorithm， when the number of clusters is 2， the proposed algorithm improves the Davies-Bouldin （DB） and Distance-based Quality Function （DQF） by about 0.25 and 8% respectively， achieving better clustering quality.
