Focusing on the research on representation learning of information networks,a metapath attribute fusion graph neural net-work(MAFGNN)based on metapath information fusion is proposed,which is to integrate the neighbor information of the target node,including the metapath information,into the node before introducing the metapath in the heterogeneous network to achieve the fusion of target node and neighbor information.This method first converts the attribute features of different types of nodes into di-mensions to facilitate subsequent fusion operations.The fusion operation of target node information is completed by calculating the weight values of target nodes and neighbor nodes.Then target nodes are fused according to specific metapaths,and finally different semantic information is fused between different metapaths.Experiments on multiple heterogeneous information datasets show that the MAFGNN model has the best performance and more accurate prediction results than the most advanced benchmark experiments in dealing with heterogeneous network node embedding.
metapathheterogeneous information networkheterogeneous graph embeddinginformation fusionattention mechanism
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