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Geom-GCN: Geometric Graph Convolutional Networks
Authors
GraphDML-UIUC-JLU: Graph-structured Data Mining and Machine Learning at University of Illinois at Urbana-Champaign (UIUC) and Jilin University (JLU) Accepted by ICLR 2020: https://openreview.net/forum?id=S1e2agrFvS AbstractMessage-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in the real world. However, two fundamental weaknesses of MPNNs' aggregators limit their ability to represent graph-structured data: losing the structural information of nodes in neighborhoods and lacking the ability to capture long-range dependencies in disassortative graphs. Few studies have noticed the weaknesses from different perspectives. From the observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome the two weaknesses. The behind basic idea is the aggregation on a graph can benefit from a continuous space underlying the graph. The proposed aggregation scheme is permutation-invariant and consists of three modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of the scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show the proposed Geom-GCN achieved state-of-the-art performance on a wide range of open datasets of graphs. If you find our paper and/or code useful in your research, please cite the following paper: @inproceedings{ICLR2020GeomGCN, title={Geom-GCN: Geometric Graph Convolutional Networks}, author={Pei, Hongbin and Wei, Bingzhe and Chang, Kevin Chen-Chuan and Lei, Yu and Yang, Bo}, booktitle={International Conference on Learning Representations (ICLR)}, year={2020} } Code Required PackagesPyTorch, NetworkX, DGL, Numpy, Scipy, Scikit-Learn, Tensorboard, TensorboardX Table 3To replicate the Geom-GCN results from Table 3, run bash NewTableThreeGeomGCN_runs.txtTo replicate the GCN results from Table 3, run bash NewTableThreeGCN_runs.txtTo replicate the GAT results from Table 3, run bash NewTableThreeGAT_runs.txtResults will be stored in runs. Combination of Embedding MethodsTo replicate the results for utilizing all embedding methods simultaneously, run bash ExperimentTwoAllGeomGCN_runs.txtResults will be stored in runs. |
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