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}.

图神经网络在节点分类任务中取得了最先进的结果。本文聚焦于多标签节点分类，通过收集和发布了三个真实的生物数据集，并开发了一个可调参数的多标签图生成器。进一步提出了适用于多标签分类的同质性和跨类邻域相似性的定义，并以 $9$ 个收集的多标签数据集为基础进行了研究。最后，通过对 $8$ 个方法在九个数据集上进行大规模对比研究，评估了目前在多标签节点分类方面的进展。代码已在 https://github.com/Tianqi-py/MLGNC 上发布。

评估图神经网络进展的数据导向方法