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

12/19/2020
by   Wentao Zhao, et al.
57

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.

READ FULL TEXT

page 8

page 9

page 11

page 12

page 15

page 16

page 17

page 18

research
06/11/2020

Data Augmentation for Graph Neural Networks

Data augmentation has been widely used to improve generalizability of ma...
research
08/25/2022

Data Augmentation for Graph Data: Recent Advancements

Graph Neural Network (GNNs) based methods have recently become a popular...
research
05/22/2023

Tokenized Graph Transformer with Neighborhood Augmentation for Node Classification in Large Graphs

Graph Transformers, emerging as a new architecture for graph representat...
research
07/11/2020

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

Graph classification, which aims to identify the category labels of grap...
research
12/16/2020

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...
research
11/07/2022

Application of Graph Neural Networks and graph descriptors for graph classification

Graph classification is an important area in both modern research and in...
research
11/05/2021

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

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

Please sign up or login with your details

Forgot password? Click here to reset