Abstract: To improve the accuracy of node importance evaluation in multiplex heterogeneous network (MHEN), a method of node importance evaluation is proposed for MHEN based on graph embedding. For the same type and different types of edges, the features of the nodes are aggregated after random walk sampling neighbor nodes, and the features are mapped to the embedding space by multi-layer perceptron to obtain the embedding vectors. Then, the node importance evaluation index for MHEN is constructed by the embedding vectors of nodes and features of local structure. The experimental results on mainstream datasets, such as CElegans and CS-Aarhus show that compared with multiplex betweenness centrality, biased PageRank and multiplex evidential centrality, the proposed method performs better in term of the accuracy.

Key words: multiplex heterogeneous network, node importance evaluation, graph embedding