  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}.
