# 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…

## 1,473 Citations

### Simple and Deep Graph Convolutional Networks

- Computer ScienceICML
- 2020

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

- Computer ScienceECAI
- 2020

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

- Computer ScienceArXiv
- 2020

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

- Computer ScienceICLR
- 2021

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

- Computer ScienceICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
- 2021

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

- Computer ScienceECML/PKDD
- 2020

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

- Computer Science
- 2022

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

- Computer ScienceArXiv
- 2019

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

- Computer ScienceArXiv
- 2022

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

- Computer ScienceApplied Intelligence
- 2021

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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