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, besides defining homophily for the multi-label scenario, we develop a new approach that dynamically fuses the feature and label correlation information to learn label-informed representations. Finally, we perform a large-scale comparative study with $10$ methods and $9$ datasets which also showcase the effectiveness of our approach. We release our benchmark at \url{https://anonymous.4open.science/r/LFLF-5D8C/}.

翻译：图神经网络（GNNs）在图的节点分类任务中已展现出最先进的性能提升。然而，这些改进主要在多类分类场景中得到验证，而每个节点可能具有多个标签的更一般且现实的场景至今鲜受关注。针对多标签节点分类开展专项研究的首要挑战在于公开可用的多标签图数据集数量有限。因此，作为我们的第一贡献，我们收集并发布了三个真实世界生物学数据集，并开发了一个多标签图生成器以生成具有可调属性的数据集。尽管高标签相似性（高同质性）通常被归因于GNNs的成功，但我们认为多标签场景并不遵循迄今为多类场景定义的同质性与异质性的常规语义。作为我们的第二贡献，除定义多标签场景下的同质性外，我们开发了一种动态融合特征与标签相关性信息的新方法，以学习标签感知表示。最后，我们使用10种方法和9个数据集进行了大规模比较研究，充分验证了我们方法的有效性。我们在\url{https://anonymous.4open.science/r/LFLF-5D8C/}发布了基准测试。