Taxonomy of Benchmarks in Graph Representation Learning

06/15/2022
by   Renming Liu, et al.
30

Graph Neural Networks (GNNs) extend the success of neural networks to graph-structured data by accounting for their intrinsic geometry. While extensive research has been done on developing GNN models with superior performance according to a collection of graph representation learning benchmarks, it is currently not well understood what aspects of a given model are probed by them. For example, to what extent do they test the ability of a model to leverage graph structure vs. node features? Here, we develop a principled approach to taxonomize benchmarking datasets according to a sensitivity profile that is based on how much GNN performance changes due to a collection of graph perturbations. Our data-driven analysis provides a deeper understanding of which benchmarking data characteristics are leveraged by GNNs. Consequently, our taxonomy can aid in selection and development of adequate graph benchmarks, and better informed evaluation of future GNN methods. Finally, our approach and implementation in package are extendable to multiple graph prediction task types and future datasets.

READ FULL TEXT

page 7

page 8

page 14

research
10/27/2021

Towards a Taxonomy of Graph Learning Datasets

Graph neural networks (GNNs) have attracted much attention due to their ...
research
10/25/2022

Benchmarking Graph Neural Networks for Internet Routing Data

The Internet is composed of networks, called Autonomous Systems (or, ASe...
research
06/30/2020

Graph Neural Networks Including Sparse Interpretability

Graph Neural Networks (GNNs) are versatile, powerful machine learning me...
research
02/28/2022

GraphWorld: Fake Graphs Bring Real Insights for GNNs

Despite advances in the field of Graph Neural Networks (GNNs), only a sm...
research
02/27/2023

A Dataset for Learning Graph Representations to Predict Customer Returns in Fashion Retail

We present a novel dataset collected by ASOS (a major online fashion ret...
research
06/16/2022

ProGNNosis: A Data-driven Model to Predict GNN Computation Time Using Graph Metrics

Graph Neural Networks (GNN) show great promise in problems dealing with ...
research
07/17/2023

Examining the Effects of Degree Distribution and Homophily in Graph Learning Models

Despite a surge in interest in GNN development, homogeneity in benchmark...

Please sign up or login with your details

Forgot password? Click here to reset