DeepAI AI Chat
Log In Sign Up

Taxonomy of Benchmarks in Graph Representation Learning

by   Renming Liu, et al.
Montréal Institute of Learning Algorithms
Michigan State University

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.


page 7

page 8

page 14


Towards a Taxonomy of Graph Learning Datasets

Graph neural networks (GNNs) have attracted much attention due to their ...

Measuring and Improving the Use of Graph Information in Graph Neural Networks

Graph neural networks (GNNs) have been widely used for representation le...

Benchmarking Graph Neural Networks for Internet Routing Data

The Internet is composed of networks, called Autonomous Systems (or, ASe...

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

Graph Neural Networks (GNN) show great promise in problems dealing with ...

GraphWorld: Fake Graphs Bring Real Insights for GNNs

Despite advances in the field of Graph Neural Networks (GNNs), only a sm...

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...

Search for the UGLE Truth: An Investigation into Unsupervised GNN Learning Environments

Graph Neural Networks (GNNs) are a pertinent tool for any machine learni...