MAGNN: Metapath Aggregated Graph Neural Network for Heterogeneous Graph Embedding

5 Feb 2020  ·  Xinyu Fu, Jiani Zhang, Ziqiao Meng, Irwin King ·

A large number of real-world graphs or networks are inherently heterogeneous, involving a diversity of node types and relation types. Heterogeneous graph embedding is to embed rich structural and semantic information of a heterogeneous graph into low-dimensional node representations. Existing models usually define multiple metapaths in a heterogeneous graph to capture the composite relations and guide neighbor selection. However, these models either omit node content features, discard intermediate nodes along the metapath, or only consider one metapath. To address these three limitations, we propose a new model named Metapath Aggregated Graph Neural Network (MAGNN) to boost the final performance. Specifically, MAGNN employs three major components, i.e., the node content transformation to encapsulate input node attributes, the intra-metapath aggregation to incorporate intermediate semantic nodes, and the inter-metapath aggregation to combine messages from multiple metapaths. Extensive experiments on three real-world heterogeneous graph datasets for node classification, node clustering, and link prediction show that MAGNN achieves more accurate prediction results than state-of-the-art baselines.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Node Clustering DBLP MAGNN NMI 80.81 # 1
ARI 85.54 # 1
Node Clustering IMDb MAGNN ARI 16.74 # 1
NMI 0.1558 # 1
Link Prediction Last.FM MAGNN AP 98.93 # 1
AUC 98.91 # 1