Simple Spectral Graph Convolution

ICLR 2021  ·  Hao Zhu, Piotr Koniusz ·

Graph Convolutional Networks (GCNs) have drawn significant attention and become promising methods for learning graph representations. The most GCNs suffer the performance loss when the depth of the model increases. Similarly to CNNs, without specially designed architectures, the performance of a network degrades quickly. Some researchers argue that the required neighbourhood size and neural network depth are two completely orthogonal aspects of graph representation. Thus, several methods extend the neighbourhood by aggregating k-hop neighbourhoods of nodes while using shallow neural networks. However, these methods still encounter oversmoothing, high computation and storage costs. In this paper, we use the Markov diffusion kernel to derive a variant of GCN called Simple Spectral Graph Convolution (S^2GC) which is closely related to spectral models and combines strengths of both spatial and spectral methods. Our spectral analysis shows that our simple spectral graph convolution used in S^2GC is a low-pass filter which partitions networks into a few large parts. Our experimental evaluation demonstrates that S^2GC with a linear learner is competitive in text and node classification tasks. Moreover, S^2GC is comparable to other state-of-the-art methods for node clustering and community prediction tasks.

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Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Text Classification 20NEWS SSGC Accuracy 88.6 # 3
Node Clustering Citeseer SSGC Accuracy 68.84 # 5
NMI 42.77 # 4
F1 64.38 # 2
Node Classification CiteSeer with Public Split: fixed 20 nodes per class SSGC Accuracy 73.6 # 12
Node Clustering Cora SSGC Accuracy 69.6 # 9
NMI 54.71 # 6
F1 65.83 # 5
Node Classification Cora: fixed 20 node per class SSGC Accuracy 83.0 # 6
Text Classification MR SSGC Accuracy 76.7 # 10
Text Classification Ohsumed SSGC Accuracy 68.5 # 3
Node Clustering Pubmed SSGC Accuracy 70.98 # 4
NMI 33.21 # 4
F1 70.28 # 2
Node Classification PubMed with Public Split: fixed 20 nodes per class SSGC Accuracy 80.4 # 13
Text Classification R52 SSGC Accuracy 94.5 # 4
Text Classification R8 SSGC Accuracy 97.4 # 12
Node Classification Reddit SSGC Accuracy 95.3 # 10
Node Clustering Wiki SSGC Accuracy 52.67 # 1
NMI 49.62 # 1
F1 44.31 # 2

Methods