Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks

Understand

While machine learning on graphs has demonstrated promise in drug design and molecular property prediction, significant benchmarking challenges hinder its further progress and relevance.

• Current benchmarking practices often lack focus on transformative, real-world applications, favoring narrow domains like two-dimensional molecular graphs over broader, impactful areas such as combinatorial optimization, relational databases, or chip design.
• Additionally, many benchmark datasets poorly represent the underlying data, leading to inadequate abstractions and misaligned use cases.
• Fragmented evaluations and an excessive focus on accuracy further exacerbate these issues, incentivizing overfitting rather than fostering generalizable insights.