Position: Graph Learning Will Lose Relevance Due To Poor Benchmarks
This addresses a critical methodological problem for researchers in graph learning, though it is an incremental critique rather than a novel solution.
This position paper argues that graph learning research is hindered by poor benchmarking practices that focus on narrow domains and inadequate datasets, preventing the development of useful graph foundation models. It calls for a paradigm shift toward more meaningful benchmarks and evaluation protocols to unlock the field's potential.
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. These limitations have prevented the development of truly useful graph foundation models. This position paper calls for a paradigm shift toward more meaningful benchmarks, rigorous evaluation protocols, and stronger collaboration with domain experts to drive impactful and reliable advances in graph learning research, unlocking the potential of graph learning.