Multi-label Node Classification On Graph-Structured Data

• URL: http://arxiv.org/abs/2304.10398v4
• Date: Thu, 29 Feb 2024 10:23:45 GMT
• Title: Multi-label Node Classification On Graph-Structured Data
• Authors: Tianqi Zhao, Ngan Thi Dong, Alan Hanjalic, Megha Khosla
• Abstract summary: Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. A more general and realistic scenario in which each node could have multiple labels has so far received little attention. We collect and release three real-world biological datasets and develop a multi-label graph generator.
• Score: 7.892731722253387
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graph Neural Networks (GNNs) have shown state-of-the-art improvements in node classification tasks on graphs. While these improvements have been largely demonstrated in a multi-class classification scenario, a more general and realistic scenario in which each node could have multiple labels has so far received little attention. The first challenge in conducting focused studies on multi-label node classification is the limited number of publicly available multi-label graph datasets. Therefore, as our first contribution, we collect and release three real-world biological datasets and develop a multi-label graph generator to generate datasets with tunable properties. While high label similarity (high homophily) is usually attributed to the success of GNNs, we argue that a multi-label scenario does not follow the usual semantics of homophily and heterophily so far defined for a multi-class scenario. As our second contribution, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected $9$ multi-label datasets. Finally, we perform a large-scale comparative study with $8$ methods and $9$ datasets and analyse the performances of the methods to assess the progress made by current state of the art in the multi-label node classification scenario. We release our benchmark at https://github.com/Tianqi-py/MLGNC.

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