R-GSN: The Relation-based Graph Similar Network for Heterogeneous Graph

Heterogeneous graph is a kind of data structure widely existing in real life. Nowadays, the research of graph neural network on heterogeneous graph has become more and more popular. The existing heterogeneous graph neural network algorithms mainly have two ideas, one is based on meta-path and the other is not. The idea based on meta-path often requires a lot of manual preprocessing, at the same time it is difficult to extend to large scale graphs. In this paper, we proposed the general heterogeneous message passing paradigm and designed R-GSN that does not need metapath, which is much improved compared to the baseline R-GCN. Experiments have shown that our R-GSN algorithm achieves the state-of-theart performance on the ogbn-mag large scale heterogeneous graph dataset.

[1]  Bernard Ghanem,et al.  FLAG: Adversarial Data Augmentation for Graph Neural Networks , 2020, ArXiv.

[2]  Yanfang Ye,et al.  Heterogeneous Graph Attention Network , 2019, WWW.

[3]  Max Welling,et al.  Modeling Relational Data with Graph Convolutional Networks , 2017, ESWC.

[4]  Nitesh V. Chawla,et al.  metapath2vec: Scalable Representation Learning for Heterogeneous Networks , 2017, KDD.

[5]  Bernard Ghanem,et al.  DeeperGCN: All You Need to Train Deeper GCNs , 2020, ArXiv.

[6]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[7]  Bernard Ghanem,et al.  DeepGCNs: Can GCNs Go As Deep As CNNs? , 2019, 2019 IEEE/CVF International Conference on Computer Vision (ICCV).

[8]  Jaewoo Kang,et al.  Graph Transformer Networks , 2019, NeurIPS.

[9]  Xavier Bresson,et al.  Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering , 2016, NIPS.

[10]  Kevin Chen-Chuan Chang,et al.  Geom-GCN: Geometric Graph Convolutional Networks , 2020, ICLR.

[11]  Yuxiao Dong,et al.  Microsoft Academic Graph: When experts are not enough , 2020, Quantitative Science Studies.

[12]  Yizhou Sun,et al.  Heterogeneous Graph Transformer , 2020, WWW.

[13]  J. Leskovec,et al.  Open Graph Benchmark: Datasets for Machine Learning on Graphs , 2020, NeurIPS.

[14]  Jure Leskovec,et al.  How Powerful are Graph Neural Networks? , 2018, ICLR.

[15]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[16]  Frank Hutter,et al.  Fixing Weight Decay Regularization in Adam , 2017, ArXiv.

[17]  Max Welling,et al.  Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.

[18]  Jure Leskovec,et al.  Inductive Representation Learning on Large Graphs , 2017, NIPS.

[19]  Pietro Liò,et al.  Graph Attention Networks , 2017, ICLR.