A data-centric approach for assessing progress of Graph Neural Networks
<|reference_start|>A data-centric approach for assessing progress of Graph Neural Networks: 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}.<|reference_end|>
@article{zhao2024a,
    title={A data-centric approach for assessing progress of Graph Neural Networks},
    author={Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla},
    journal={Published in Data-centric Machine Learning Research Worshop @ ICML
  2024},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.12439},
    primaryClass={cs.LG}
}