# A data-centric approach for assessing progress of Graph Neural Networks

arXiv: https://arxiv.org/abs/2406.12439 · HTML: https://mlsanity.com/paper/2406.12439/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 8 references that are in the map.
- First posted on arXiv 18 Jun 2024.

## Builds on (most cited first, 3 of 3)

- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification](https://mlsanity.com/paper/1910.09706/) (2019)

## Abstract (arXiv)

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 urlhttps://github.com/Tianqi-py/MLGNC.