A data-centric approach for assessing progress of Graph Neural Networks

• URL: http://arxiv.org/abs/2406.12439v1
• Date: Tue, 18 Jun 2024 09:41:40 GMT
• Title: A data-centric approach for assessing progress of Graph Neural Networks
• Authors: Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla,
• Abstract summary: Graph Neural Networks (GNNs) have achieved state-of-the-art results in node classification tasks. Most improvements are in multi-class classification, with less focus on the cases where each node could have multiple labels. First challenge in studying multi-label node classification is the scarcity of publicly available datasets.
• Score: 7.2249434861826325
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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}.

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