# Multi-label Node Classification On Graph-Structured Data

arXiv: https://arxiv.org/abs/2304.10398 · HTML: https://mlsanity.com/paper/2304.10398/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 55 references that are in the map.
- First posted on arXiv 20 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Inductive Representation Learning on Large Graphs](https://mlsanity.com/paper/1706.02216/) (2017)
- [Open Graph Benchmark: Datasets for Machine Learning on Graphs](https://mlsanity.com/paper/2005.00687/) (2020)
- [DeepWalk: Online Learning of Social Representations](https://mlsanity.com/paper/1403.6652/) (2014)
- [GraphSAINT: Graph Sampling Based Inductive Learning Method](https://mlsanity.com/paper/1907.04931/) (2019)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Is Homophily a Necessity for Graph Neural Networks?](https://mlsanity.com/paper/2106.06134/) (2021)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Unifying Graph Convolutional Neural Networks and Label Propagation](https://mlsanity.com/paper/2002.06755/) (2020)
- [Extract the Knowledge of Graph Neural Networks and Go Beyond it: An Effective Knowledge Distillation Framework](https://mlsanity.com/paper/2103.02885/) (2021)
- [Evaluating Link Prediction Methods](https://mlsanity.com/paper/1505.04094/) (2015)
- [Neural Message Passing for Multi-Label Classification](https://mlsanity.com/paper/1904.08049/) (2019)
- [Node Representation Learning for Directed Graphs](https://mlsanity.com/paper/1810.09176/) (2018)
- [Multi-Label Text Classification using Attention-based Graph Neural Network](https://mlsanity.com/paper/2003.11644/) (2020)
- [Multi-Label Graph Convolutional Network Representation Learning](https://mlsanity.com/paper/1912.11757/) (2019)
- [CopulaGNN: Towards Integrating Representational and Correlational Roles of Graphs in Graph Neural Networks](https://mlsanity.com/paper/2010.02089/) (2020)
- [Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification](https://mlsanity.com/paper/1910.09706/) (2019)
- [Ranking-Based Autoencoder for Extreme Multi-label Classification](https://mlsanity.com/paper/1904.05937/) (2019)
- [GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification](https://mlsanity.com/paper/2012.05860/) (2020)
- [Homophily as a Process Generating Social Networks: Insights from Social Distance Attachment Model](https://mlsanity.com/paper/1907.07055/) (2019)

## Built on by (most cited first, 10 of 10)

- [GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks](https://mlsanity.com/paper/2310.03399/) (2023)
- [Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks](https://mlsanity.com/paper/2402.15680/) (2024)
- [AGALE: A Graph-Aware Continual Learning Evaluation Framework](https://mlsanity.com/paper/2406.01229/) (2024)
- [Disentangled and Self-Explainable Node Representation Learning](https://mlsanity.com/paper/2410.21043/) (2024)
- [Multi-Label Node Classification with Label Influence Propagation](https://mlsanity.com/paper/2607.00671/) (2026)
- [Extending Graph Condensation to Multi-Label Datasets: A Benchmark Study](https://mlsanity.com/paper/2412.17961/) (2024)
- [Population-Scale Network Embeddings Expose Educational Divides in Network Structure Related to Right-Wing Populist Voting](https://mlsanity.com/paper/2508.21236/) (2025)
- [Rethinking Multi-Label Node Classification: Do Tuned Classic GNNs Suffice?](https://mlsanity.com/paper/2605.01403/) (2026)
- [MLGIB: Multi-Label Graph Information Bottleneck for Expressive and Robust Message Passing](https://mlsanity.com/paper/2605.13126/) (2026)
- [Self-Explainable Multi-Label Graph Neural Network for Correlated Evidence Attribution](https://mlsanity.com/paper/2608.27574/) (2026)

## Abstract (arXiv)

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, we define homophily and Cross-Class Neighborhood Similarity for the multi-label scenario and provide a thorough analyses of the collected 9 multi-label datasets. Finally, we perform a large-scale comparative study with 8 methods and 9 datasets and analyse the performances of the methods to assess the progress made by current state of the art in the multi-label node classification scenario. We release our benchmark at https://github.com/Tianqi-py/MLGNC.