The invention discloses a graph neural
network link prediction method based on multi-dimensional similarity, and aims to improve the accuracy and generalization ability of missing link prediction in a
complex network. The method is suitable for social networks,
citation networks, recommendation systems and other actual scenes with isomerism, sparsity and dynamic evolution characteristics. In order to solve the problem that a traditional method only depends on local adjacency information or single topological similarity and is difficult to capture a high-
order structure relation and multi-dimensional feature association, a unified measurement
system fusing structure similarity, attribute similarity and path similarity is constructed, and potential association between nodes is deeply mined. By introducing a self-adaptive feature weighting mechanism, the model can dynamically adjust the multi-dimensional similarity fusion weight according to network features, and the expression and distinguishing capability of the node relationship is enhanced. On the basis, the deep representation learning
advantage of the graph neural network is combined, a model structure with the selective
feature fusion capability is designed, and precise modeling of a complex
link generation mechanism is achieved. The method has good expandability and
interpretability, and prediction deviation caused by network heterogeneity can be effectively relieved. Experimental results show that the method is obviously superior to the existing mainstream method on a plurality of real
network data sets, and has high theoretical value and wide application prospect.