Abstract:To solve the problem of missing structural information and other meta-path semantic information in heterogeneous network representation based on single meta-path, this study proposes a representation learning method of heterogeneous network based on fusion meta-path weight. This method learns from the set of meta-paths in heterogeneous information networks, and then the low-dimensional representations of different meta-paths are fused with appropriate weights. The representation of heterogeneous networks with semantic information of different meta-paths are obtained. Experiments show that the heterogeneous network representation learning based on fusion meta-path weights has sound representation learning ability and can be effectively applied to data mining.