# Message Passing Attention Networks for Document Understanding

arXiv: https://arxiv.org/abs/1908.06267 · HTML: https://mlsanity.com/paper/1908.06267/

## Key facts

- Cited by 23 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Aug 2019.

## Built on by (most cited first, 23 of 23)

- [Dynamic Neural Networks: A Survey](https://mlsanity.com/paper/2102.04906/) (2021)
- [Graph-Guided Network for Irregularly Sampled Multivariate Time Series](https://mlsanity.com/paper/2110.05357/) (2021)
- [Augmentations in Graph Contrastive Learning: Current Methodological Flaws & Towards Better Practices](https://mlsanity.com/paper/2111.03220/) (2021)
- [InducT-GCN: Inductive Graph Convolutional Networks for Text Classification](https://mlsanity.com/paper/2206.00265/) (2022)
- [Graph Neural Networks for Text Classification: A Survey](https://mlsanity.com/paper/2304.11534/) (2023)
- [Re-evaluating Word Mover's Distance](https://mlsanity.com/paper/2105.14403/) (2021)
- [A Survey and Empirical Evaluation of Parallel Deep Learning Frameworks](https://mlsanity.com/paper/2111.04949/) (2021)
- [Predicting Patient Readmission Risk from Medical Text via Knowledge Graph Enhanced Multiview Graph Convolution](https://mlsanity.com/paper/2201.02510/) (2021)
- [Unsupervised Sentence Textual Similarity with Compositional Phrase Semantics](https://mlsanity.com/paper/2210.02284/) (2022)
- [Graph with Sequence: Broad-Range Semantic Modeling for Fake News Detection](https://mlsanity.com/paper/2412.05672/) (2024)
- [Hawk: An Industrial-strength Multi-label Document Classifier](https://mlsanity.com/paper/2301.06057/) (2023)
- [Predicting multiple sclerosis disease severity with multimodal deep neural networks](https://mlsanity.com/paper/2304.04062/) (2023)
- [BHGNN-RT: Network embedding for directed heterogeneous graphs](https://mlsanity.com/paper/2311.14404/) (2023)
- [NLP Research and Resources at DaSciM, Ecole Polytechnique](https://mlsanity.com/paper/2112.00566/) (2021)
- [Detecting Backdoors in Deep Text Classifiers](https://mlsanity.com/paper/2210.11264/) (2022)
- [FewShotTextGCN: K-hop neighborhood regularization for few-shot learning on graphs](https://mlsanity.com/paper/2301.10481/) (2023)
- [Connecting the Dots: What Graph-Based Text Representations Work Best for Text Classification Using Graph Neural Networks?](https://mlsanity.com/paper/2305.14578/) (2023)
- [Supervised Attention Using Homophily in Graph Neural Networks](https://mlsanity.com/paper/2307.05217/) (2023)
- [Graph Neural Machine: A New Model for Learning with Tabular Data](https://mlsanity.com/paper/2402.02862/) (2024)
- [CiliaGraph: Enabling Expression-enhanced Hyper-Dimensional Computation in Ultra-Lightweight and One-Shot Graph Classification on Edge](https://mlsanity.com/paper/2405.19033/) (2024)
- [DocNet: Semantic Structure in Inductive Bias Detection Models](https://mlsanity.com/paper/2406.10965/) (2024)
- [Graph Neural Networks on Discriminative Graphs of Words](https://mlsanity.com/paper/2410.20469/) (2024)
- [On the Lipschitz Continuity of Set Aggregation Functions and Neural Networks for Sets](https://mlsanity.com/paper/2505.24403/) (2025)

## Abstract (arXiv)

Graph neural networks have recently emerged as a very effective framework for processing graph-structured data. These models have achieved state-of-the-art performance in many tasks. Most graph neural networks can be described in terms of message passing, vertex update, and readout functions. In this paper, we represent documents as word co-occurrence networks and propose an application of the message passing framework to NLP, the Message Passing Attention network for Document understanding (MPAD). We also propose several hierarchical variants of MPAD. Experiments conducted on 10 standard text classification datasets show that our architectures are competitive with the state-of-the-art. Ablation studies reveal further insights about the impact of the different components on performance. Code is publicly available at: https://github.com/giannisnik/mpad .