MIED : An Improved Graph Neural Network for Node Embedding in Heterogeneous Graphs

Abstract

This paper proposes a Metapath-Infused Exponential Decay graph neural network (MIED) approach for node embedding in heterogeneous graphs. It is designed to address limitations in existing methods, which usually lose the graph information during feature alignment and ignore the different importance of nodes during metapath aggregation. Firstly, graph convolutional network (GCN) is applied on the subgraphs, which is derived from the original graph with given metapaths to transform node features. Secondly, an exponential decay encoder (EDE) is designed, in which the influence of nodes on starting point decays exponentially with a fixed parameter as they move farther away from it. Thirdly, a set of experiments is conducted on two selected datasets of heterogeneous graphs, i.e., IMDb and DBLP, for comparison purposes. The results show that MIED outperforms selected approaches, e.g., GAT, HAN, MAGNN, etc. Thus, our approach is proven to be able to take full advantage of graph information considering node weights based on distance aspects. Finally, relevant parameters are analyzed and the recommended hyperparameter setting is given.