GraphLand: Evaluating Graph Machine Learning Models on Diverse Industrial Data

ArXi:2409.14500v5 Announce Type: replace Although data that can be naturally represented as graphs is widespread in real-world applications across diverse industries, popular graph ML benchmarks for node property prediction only cover a surprisingly narrow set of data domains, and graph neural networks (GNNs) are often evaluated on just a few academic citation networks. This issue is particularly pressing in light of the recent growing interest in designing graph foundation models.