Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. However, most improvements are in multi-class classification, with less focus on the cases where each node could have multiple labels. The first challenge in studying multi-label node classification is the scarcity of publicly available datasets. To address this, we collected and released three real-world biological datasets and developed a multi-label graph generator with tunable properties. We also argue that traditional notions of homophily and heterophily do not apply well to multi-label scenarios. Therefore, we define homophily and Cross-Class Neighborhood Similarity for multi-label classification and investigate 9 collected multi-label datasets. Lastly, we conducted a large-scale comparative study with 8 methods across nine datasets to evaluate current progress in multi-label node classification. We release our code at \url{https://github.com/Tianqi-py/MLGNC}.
@article{arxiv.2406.12439,
title = {A data-centric approach for assessing progress of Graph Neural Networks},
author = {Tianqi Zhao and Ngan Thi Dong and Alan Hanjalic and Megha Khosla},
journal= {arXiv preprint arXiv:2406.12439},
year = {2024}
}