• Corpus ID: 67752026

Simplifying Graph Convolutional Networks

@article{Wu2019SimplifyingGC,
  title={Simplifying Graph Convolutional Networks},
  author={Felix Wu and Tianyi Zhang and Amauri H. de Souza and Christopher Fifty and Tao Yu and Kilian Q. Weinberger},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.07153}
}
Graph Convolutional Networks (GCNs) and their variants have experienced significant attention and have become the de facto methods for learning graph representations. [] Key Method 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…
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1,473 Citations
Highly Influential Citations
440
Background Citations
769
Methods Citations
691
Results Citations
53

1,473 Citations

Simple and Deep Graph Convolutional Networks

The GCNII is proposed, an extension of the vanilla GCN model with two simple yet effective techniques: {\em Initial residual} and {\em Identity mapping} that effectively relieves the problem of over-smoothing.

Simplifying Graph Attention Networks with Source-Target Separation

We present a novel Graph Neural Networks (GNN) architecture as an simplification of Graph Attentional Network (GAT) model with implicit computation of edge attention coefficients and shared…

A Graph Convolutional Network Composition Framework for Semi-supervised Classification

The empirical experimental results suggest that several newly composed variants of graph convolutional networks are useful alternatives to consider because they are as competitive as, or better than, the original GCN.

Simple Spectral Graph Convolution

This paper uses a modified Markov Diffusion Kernel to derive a variant of GCN called Simple Spectral Graph Convolution (S2GC), and spectral analysis shows that the simple spectral graph convolution used in S2GC is a trade-off of low and high-pass filter bands which capture the global and local contexts of each node.

Non-Recursive Graph Convolutional Networks

This paper proposes a novel architecture named Non-Recursive Graph Convolutional Network (NRGCN) to improve both the training efficiency and the learning performance of GCNs in the context of node classification, and proposes to represent different hops of neighbors for each node based on inner-layer aggregation and layer-independent sampling.

Graph-Revised Convolutional Network

A GCN-based graph revision module is introduced for predicting missing edges and revising edge weights w.r.t. downstream tasks via joint optimization, which shows that GRCN consistently outperforms strong baseline methods by a large margin.

Analysis of Graph Convolutional Networks using Neural Tangent Kernels

This paper derives NTKs corresponding to infinitely wide GCNs with and without skip connections and allowing non-linear output layer, and shows empirically that the approximation is similar to linear output layer.

Keep It Simple: Graph Autoencoders Without Graph Convolutional Networks

This paper proposes to replace the GCN encoder by a simple linear model w.r.t. the adjacency matrix of the graph, and empirically shows that this approach consistently reaches competitive performances on challenging tasks such as link prediction and node clustering.

Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision

It is shown empirically that DSGNNs are resilient to over-smoothing and can outperform competitive benchmarks on node and graph property prediction problems.

Simplified multilayer graph convolutional networks with dropout

This paper presents simplified multilayer graph convolutional networks with dropout (DGCs), novel neural network architectures that successively perform nonlinearity removal and weight matrix merging between graph conventional layers, leveraging a dropout layer to achieve feature augmentation and effectively reduce overfitting.
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