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(GNN)已经在图上的节点分类任务中展示出了最新的改进。虽然在多类分类场景中,这些改进已经得到了广泛的证明,但更一般和现实的情况是每个节点可能会有多个标签但迄今为止却受到了很少的关注。关于多标签节点分类的着手研究面临的第一个挑战是公开的可用的多标签图数据集数量有限。因此,作为我们的第一个贡献,我们收集并发布了三个实际的生物数据集,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。虽然高标签相似度(高同质性)通常被归因于GNN的成功,但我们认为多标签情况并不遵循到目前为止为多类情况所定义的同质性和异质性的常规语义。作为我们的第二个贡献,除了为多标签场景定义同质性之外,我们还开发了一种新的方法,动态融合特征和标签相关信息,以学习标签相关表示。最后,我们进行了一个大规模的比较研究,涉及10种方法和9个数据集,同时展示了我们方法的有效性。我们在\url{https://anonymous.4open.science/}上发布了我们的基准的具体信息。