Simplifying Graph Convolutional Networks

Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. GCNs derive inspiration primarily from recent deep learning approaches, and as a result, may inherit unnecessary complexity and redundant computation. In this paper, we reduce this excess complexity through successively removing nonlinearities and collapsing weight matrices between consecutive layers. We theoretically analyze the resulting linear model and show that it corresponds to a fixed low-pass filter followed by a linear classifier. Notably, our experimental evaluation demonstrates that these simplifications do not negatively impact accuracy in many downstream applications. Moreover, the resulting model scales to larger datasets, is naturally interpretable, and yields up to two orders of magnitude speedup over FastGCN.

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Results from the Paper


Ranked #3 on Text Classification on 20NEWS (using extra training data)

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Task Dataset Model Metric Name Metric Value Global Rank Uses Extra
Training Data
Result Benchmark
Text Classification 20NEWS SGC Accuracy 88.5 # 3
Text Classification 20NEWS SGCN Accuracy 88.5 # 3
Graph Regression Lipophilicity SGC RMSE 0.998 # 11
Sentiment Analysis MR SGCN Accuracy 75.9 # 16
Sentiment Analysis MR SGC Accuracy 75.9 # 16
Node Property Prediction ogbn-papers100M SGC Test Accuracy 0.6329 ± 0.0019 # 17
Validation Accuracy 0.6648 ± 0.0020 # 17
Number of params 144044 # 17
Ext. data No # 1
Text Classification Ohsumed SGCN Accuracy 68.5 # 4
Text Classification Ohsumed SGC Accuracy 68.5 # 4
Text Classification R52 SGC Accuracy 94.0 # 5
Text Classification R52 SGCN Accuracy 94.0 # 5
Text Classification R8 SGCN Accuracy 97.2 # 7
Text Classification R8 SGC Accuracy 97.2 # 7
Skeleton Based Action Recognition SBU SGCConv Accuracy 94.0% # 7
Relation Extraction TACRED C-SGC F1 67.0 # 28

Methods