# Graph Neural Networks for Protein-Protein Interactions -- A Short Survey

arXiv: https://arxiv.org/abs/2404.10450 · HTML: https://mlsanity.com/paper/2404.10450/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 16 Apr 2024.

## Builds on (most cited first, 5 of 5)

- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations](https://mlsanity.com/paper/1906.05017/) (2019)
- [SkipGNN: Predicting Molecular Interactions with Skip-Graph Networks](https://mlsanity.com/paper/2004.14949/) (2020)
- [Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction](https://mlsanity.com/paper/2105.06709/) (2021)
- [SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2305.08316/) (2023)

## Built on by (most cited first, 3 of 3)

- [Towards Efficient Training of Graph Neural Networks: A Multiscale Approach](https://mlsanity.com/paper/2503.19666/) (2025)
- [A Multimodal Human Protein Embeddings Database: DeepDrug Protein Embeddings Bank (DPEB)](https://mlsanity.com/paper/2510.22008/) (2025)
- [A Counterfactual Graph Approach to Oppositional Narrative Analysis](https://mlsanity.com/paper/2603.06135/) (2026)

## Abstract (arXiv)

Protein-protein interactions (PPIs) play key roles in a broad range of biological processes. Numerous strategies have been proposed for predicting PPIs, and among them, graph-based methods have demonstrated promising outcomes owing to the inherent graph structure of PPI networks. This paper reviews various graph-based methodologies, and discusses their applications in PPI prediction. We classify these approaches into two primary groups based on their model structures. The first category employs Graph Neural Networks (GNN) or Graph Convolutional Networks (GCN), while the second category utilizes Graph Attention Networks (GAT), Graph Auto-Encoders and Graph-BERT. We highlight the distinctive methodologies of each approach in managing the graph-structured data inherent in PPI networks and anticipate future research directions in this domain.