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 https://anonymous.4open.science/r/LFLF-5D8C/.

该论文介绍了一种新的方法，可以在多标签节点分类任务中使用图神经网络，并发布了三个真实世界的生物学数据集，用于该任务的基准测试。作者还定义了多标签场景下的同质性概念，并开发了一种新的方法来动态融合特征和标签相关信息，从而学习标签感知的表示。经过大规模对比研究，该方法在九个数据集上表现良好，并证明了其有效性。