A pipeline for fair comparison of graph neural networks in node classification tasks

by   Wentao Zhao, et al.

Graph neural networks (GNNs) have been investigated for potential applicability in multiple fields that employ graph data. However, there are no standard training settings to ensure fair comparisons among new methods, including different model architectures and data augmentation techniques. We introduce a standard, reproducible benchmark to which the same training settings can be applied for node classification. For this benchmark, we constructed 9 datasets, including both small- and medium-scale datasets from different fields, and 7 different models. We design a k-fold model assessment strategy for small datasets and a standard set of model training procedures for all datasets, enabling a standard experimental pipeline for GNNs to help ensure fair model architecture comparisons. We use node2vec and Laplacian eigenvectors to perform data augmentation to investigate how input features affect the performance of the models. We find topological information is important for node classification tasks. Increasing the number of model layers does not improve the performance except on the PATTERN and CLUSTER datasets, in which the graphs are not connected. Data augmentation is highly useful, especially using node2vec in the baseline, resulting in a substantial baseline performance improvement.


page 8

page 9

page 11

page 12

page 15

page 16

page 17

page 18


Data Augmentation for Graph Neural Networks

Data augmentation has been widely used to improve generalizability of ma...

Data Augmentation for Neural Online Chat Response Selection

Data augmentation seeks to manipulate the available data for training to...

M-Evolve: Structural-Mapping-Based Data Augmentation for Graph Classification

Graph classification, which aims to identify the category labels of grap...

A Deep Graph Neural Networks Architecture Design: From Global Pyramid-like Shrinkage Skeleton to Local Topology Link Rewiring

Expressivity plays a fundamental role in evaluating deep neural networks...

Augmentations in Graph Contrastive Learning: Current Methodological Flaws Towards Better Practices

Graph classification has applications in bioinformatics, social sciences...

Trainability for Universal GNNs Through Surgical Randomness

Message passing neural networks (MPNN) have provable limitations, which ...

Degree-Quant: Quantization-Aware Training for Graph Neural Networks

Graph neural networks (GNNs) have demonstrated strong performance on a w...