%0 Journal Article %A LU Furong %A YUAN Zhi'an %A QIAN Yuhua %T Link Prediction Combining Motif Graph Neural Network and Auto-encoder %D 2023 %R 10.3778/j.issn.1673-9418.2104085 %J Journal of Frontiers of Computer Science & Technology %P 209-216 %V 17 %N 1 %X Link prediction is a basic task of network data mining, and there are many related research results. Due to the in-depth development of graph neural network research, the related models can learn the important features of link prediction more effectively, and thus achieve better results. However, different from the CNN model in deep learning, the most existing graph neural network models only aggregate the first-order neighbor information of nodes, and do not fully consider the topology characteristics between neighbors. On this basis, this paper proposes a link prediction model based on motif graph neural network. The model adopts auto-encoder architecture. In the encoding process, the adjacent matrix of the node is constructed by the motif, and then the motif neighborhood of the node is obtained. According to the neighborhood of each kind of motif, the neighbor information is aggregated, and the representation of the node is obtained by nonlinear transformation. Finally, the representation of the node under each kind of motif is concatenated. However, owing to different importance of different motifs in the network, the attention weights of different motifs are given by using the attention network, and the vector representation of nodes is given by connecting the attention network. In the decoding process, the network is reconstructed by calculating the similarity between nodes. Experimental results on several citation networks show that the proposed method outperforms most benchmark algorithms in two indices, and effectively improves the accuracy of link prediction on networks. %U http://fcst.ceaj.org/EN/10.3778/j.issn.1673-9418.2104085