Embedding Learning Algorithm for Heterogeneous Network Based on Meta-Graph Convolution

Heterogeneous network embedding is to embed the rich structural and semantic information of heterogeneous networks into the low dimensional node representations. Graph convolutional networks are effective methods to process network data, and they are also used to research the representation of multi-type nodes and multi-dimensional relationships of heterogeneous networks. The existing graph convolutional network models mainly use meta-path to represent semantic relationship between nodes with different types. However, a single meta-path cannot accurately characterize the specific complex semantics between nodes, that is, it cannot make full use of high-order indirect semantic relationship between nodes. To address the above limitations, it is proposed that an embedding learning algorithm for heterogeneous network, named MGCN(meta-graph convolutional network). The algorithm includes two stages of heterogeneous adjacency matrices calculation based on meta-graph and learning node embedding. The heterogeneous adjacency matrix fuses different semantic information from multiple meta-paths and mines high-order indirect relationship between nodes. In addition, it can aggregate the neighborhood features of nodes into a unified pattern. This method reduces the embedding dimension, and then reduces the calculation time. Extensive experiments on two public heterogeneous network datasets show that the proposed MGCN can outperform baselines in basic research tasks of social computing like node classification and need less model training time.