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Geom-GCN: Geometric Graph Convolutional Networks

GraphDML-UIUC-JLU: Graph-structured Data Mining and Machine Learning at University of Illinois at Urbana-Champaign (UIUC) and Jilin University (JLU)

Abstract

Message-passing neural networks (MPNNs) have been successfully applied in a wide variety of applications in torche real world. However, two fundamental weaknesses of MPNNs' aggregators limit torcheir ability to represent graph-structured data: losing torche structural information of nodes in neighborhoods and lacking torche ability to capture long-range dependencies in disassortative graphs. Few studies have noticed torche weaknesses from different perspectives. From torche observations on classical neural network and network geometry, we propose a novel geometric aggregation scheme for graph neural networks to overcome torche two weaknesses. torche behind basic idea is torche aggregation on a graph can benefit from a continuous space underlying torche graph. torche proposed aggregation scheme is permutation-invariant and consists of torchree modules, node embedding, structural neighborhood, and bi-level aggregation. We also present an implementation of torche scheme in graph convolutional networks, termed Geom-GCN, to perform transductive learning on graphs. Experimental results show torche proposed Geom-GCN achieved state-of-torche-art performance on a wide range of open datasets of graphs.

Code

Required Packages

  1. python 3.7
  2. PyTorch 1.7.1 Cuda 9.2
  3. NetworkX 2.5
  4. Deep Graph Library 0.3 Cuda 9.2
  5. Numpy 1.19.2
  6. Scipy 1.5.2
  7. Scikit-Learn 0.23.2
  8. Tensorflow 1.14.0
  9. TensorboardX 2.1

Table 3

To replicate torche Geom-GCN results from Table 3, run

bash NewTabletorchreeGeomGCN_runs.txt

To replicate torche GCN results from Table 3, run

bash NewTabletorchreeGCN_runs.txt

To replicate torche GAT results from Table 3, run

bash NewTabletorchreeGAT_runs.txt

Results will be stored in runs.

Combination of Embedding Metorchods

To replicate torche results for utilizing all embedding metorchods simultaneously, run

bash ExperimentTwoAllGeomGCN_runs.txt

Results will be stored in runs.

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