# SemiGNN-PPI: Self-Ensembling Multi-Graph Neural Network for Efficient and Generalizable Protein-Protein Interaction Prediction

arXiv: https://arxiv.org/abs/2305.08316 · HTML: https://mlsanity.com/paper/2305.08316/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 59 references that are in the map.
- First posted on arXiv 15 May 2023.

## Builds on (most cited first, 18 of 18)

- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [How Powerful are Graph Neural Networks?](https://mlsanity.com/paper/1810.00826/) (2018)
- [Graph Contrastive Learning with Augmentations](https://mlsanity.com/paper/2010.13902/) (2020)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation](https://mlsanity.com/paper/1908.10454/) (2019)
- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [Self-Ensembling with GAN-based Data Augmentation for Domain Adaptation in Semantic Segmentation](https://mlsanity.com/paper/1909.00589/) (2019)
- [The Emerging Trends of Multi-Label Learning](https://mlsanity.com/paper/2011.11197/) (2020)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [Deep Learning in Bioinformatics](https://mlsanity.com/paper/1603.06430/) (2016)
- [LE-UDA: Label-efficient unsupervised domain adaptation for medical image segmentation](https://mlsanity.com/paper/2212.02078/) (2022)
- [Learning Unknown from Correlations: Graph Neural Network for Inter-novel-protein Interaction Prediction](https://mlsanity.com/paper/2105.06709/) (2021)
- [MT-UDA: Towards Unsupervised Cross-modality Medical Image Segmentation with Limited Source Labels](https://mlsanity.com/paper/2203.12454/) (2022)
- [Semi-supervised Models are Strong Unsupervised Domain Adaptation Learners](https://mlsanity.com/paper/2106.00417/) (2021)
- [Distilling Knowledge from Self-Supervised Teacher by Embedding Graph Alignment](https://mlsanity.com/paper/2211.13264/) (2022)
- [Object-Aware Self-supervised Multi-Label Learning](https://mlsanity.com/paper/2205.07028/) (2022)

## Built on by (most cited first, 13 of 13)

- [MAPE-PPI: Towards Effective and Efficient Protein-Protein Interaction Prediction via Microenvironment-Aware Protein Embedding](https://mlsanity.com/paper/2402.14391/) (2024)
- [ProLLM: Protein Chain-of-Thoughts Enhanced LLM for Protein-Protein Interaction Prediction](https://mlsanity.com/paper/2405.06649/) (2024)
- [Semi-Supervised Crowd Counting with Contextual Modeling: Facilitating Holistic Understanding of Crowd Scenes](https://mlsanity.com/paper/2310.10352/) (2023)
- [Multi-level Interaction Modeling for Protein Mutational Effect Prediction](https://mlsanity.com/paper/2405.17802/) (2024)
- [Graph Neural Networks for Protein-Protein Interactions -- A Short Survey](https://mlsanity.com/paper/2404.10450/) (2024)
- [MTPNet: Multi-Grained Target Perception for Unified Activity Cliff Prediction](https://mlsanity.com/paper/2506.05427/) (2025)
- [Deep Manifold Transformation for Protein Representation Learning](https://mlsanity.com/paper/2402.09416/) (2024)
- [Graph Similarity Computation via Interpretable Neural Node Alignment](https://mlsanity.com/paper/2412.12185/) (2024)
- [CAME-AB: Cross-Modality Attention with Mixture-of-Experts for Antibody Binding Site Prediction](https://mlsanity.com/paper/2509.06465/) (2025)
- [A Survey of Heterogeneous Graph Neural Networks for Cybersecurity Anomaly Detection](https://mlsanity.com/paper/2510.26307/) (2025)
- [BadImplant: Injection-based Multi-Targeted Graph Backdoor Attack](https://mlsanity.com/paper/2601.15474/) (2026)
- [Learning the Interaction Prior for Protein-Protein Interaction Prediction: A Model-Agnostic Approach](https://mlsanity.com/paper/2605.09964/) (2026)
- [Enhancing Protein-Protein Interaction Prediction with Hierarchical Motif-based Multimodal Protein Embedding](https://mlsanity.com/paper/2606.02629/) (2026)

## Abstract (arXiv)

Protein-protein interactions (PPIs) are crucial in various biological processes and their study has significant implications for drug development and disease diagnosis. Existing deep learning methods suffer from significant performance degradation under complex real-world scenarios due to various factors, e.g., label scarcity and domain shift. In this paper, we propose a self-ensembling multigraph neural network (SemiGNN-PPI) that can effectively predict PPIs while being both efficient and generalizable. In SemiGNN-PPI, we not only model the protein correlations but explore the label dependencies by constructing and processing multiple graphs from the perspectives of both features and labels in the graph learning process. We further marry GNN with Mean Teacher to effectively leverage unlabeled graph-structured PPI data for self-ensemble graph learning. We also design multiple graph consistency constraints to align the student and teacher graphs in the feature embedding space, enabling the student model to better learn from the teacher model by incorporating more relationships. Extensive experiments on PPI datasets of different scales with different evaluation settings demonstrate that SemiGNN-PPI outperforms state-of-the-art PPI prediction methods, particularly in challenging scenarios such as training with limited annotations and testing on unseen data.