Understanding Isomorphism Bias in Graph Data Sets

Understand

In recent years there has been a rapid increase in classification methods on graph structured data.

• Both in graph kernels and graph neural networks, one of the implicit assumptions of successful state-of-the-art models was that incorporating graph isomorphism features into the architecture leads to better empirical performance.
• However, as we discover in this work, commonly used data sets for graph classification have repeating instances which cause the problem of isomorphism bias, i.e.
• artificially increasing the accuracy of the models by memorizing target information from the training set.