# Bipartite Graph Embedding via Mutual Information Maximization

arXiv: https://arxiv.org/abs/2012.05442 · HTML: https://mlsanity.com/paper/2012.05442/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Dec 2020.

## Built on by (most cited first, 29 of 29)

- [Graph Self-Supervised Learning: A Survey](https://mlsanity.com/paper/2103.00111/) (2021)
- [Self-supervised Learning on Graphs: Contrastive, Generative,or Predictive](https://mlsanity.com/paper/2105.07342/) (2021)
- [LLM-based Agents Suffer from Hallucinations: A Survey of Taxonomy, Methods, and Directions](https://mlsanity.com/paper/2509.18970/) (2025)
- [Masked Contrastive Graph Representation Learning for Age Estimation](https://mlsanity.com/paper/2306.17798/) (2023)
- [Towards Graph Contrastive Learning: A Survey and Beyond](https://mlsanity.com/paper/2405.11868/) (2024)
- [Graph Neural Networks: Methods, Applications, and Opportunities](https://mlsanity.com/paper/2108.10733/) (2021)
- [COIN: Co-Cluster Infomax for Bipartite Graphs](https://mlsanity.com/paper/2206.00006/) (2022)
- [STERLING: Synergistic Representation Learning on Bipartite Graphs](https://mlsanity.com/paper/2302.05428/) (2023)
- [Deep Structural Point Process for Learning Temporal Interaction Networks](https://mlsanity.com/paper/2107.03573/) (2021)
- [Aligning Recommendation and Conversation via Dual Imitation](https://mlsanity.com/paper/2211.02848/) (2022)
- [Enhancing Content-based Recommendation via Large Language Model](https://mlsanity.com/paper/2404.00236/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Features Based Adaptive Augmentation for Graph Contrastive Learning](https://mlsanity.com/paper/2207.01792/) (2022)
- [SINCERE: Sequential Interaction Networks representation learning on Co-Evolving RiEmannian manifolds](https://mlsanity.com/paper/2305.03883/) (2023)
- [Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysis](https://mlsanity.com/paper/2308.00404/) (2023)
- [Contrastive Sequential Interaction Network Learning on Co-Evolving Riemannian Spaces](https://mlsanity.com/paper/2401.01243/) (2024)
- [Effective Edge-wise Representation Learning in Edge-Attributed Bipartite Graphs](https://mlsanity.com/paper/2406.13369/) (2024)
- [Graph Prompting for Graph Learning Models: Recent Advances and Future Directions](https://mlsanity.com/paper/2506.08326/) (2025)
- [GegenNet: Spectral Convolutional Neural Networks for Link Sign Prediction in Signed Bipartite Graphs](https://mlsanity.com/paper/2508.19907/) (2025)
- [Models and Benchmarks for Representation Learning of Partially Observed Subgraphs](https://mlsanity.com/paper/2209.00508/) (2022)
- [Link Prediction in Bipartite Networks](https://mlsanity.com/paper/2406.06658/) (2024)
- [User Embedding based Neighborhood Aggregation Method for Inductive Recommendation](https://mlsanity.com/paper/2102.07575/) (2021)
- [NumCoKE: Ordinal-Aware Numerical Reasoning over Knowledge Graphs with Mixture-of-Experts and Contrastive Learning](https://mlsanity.com/paper/2411.12950/) (2024)
- [Prototypical Contrastive Learning through Alignment and Uniformity for Recommendation](https://mlsanity.com/paper/2402.02079/) (2024)
- [A Survey of Latent Factor Models in Recommender Systems](https://mlsanity.com/paper/2405.18068/) (2024)
- [Towards Deeper Understanding of PPR-based Embedding Approaches: A Topological Perspective](https://mlsanity.com/paper/2405.19649/) (2024)
- [Multiplex Graph Contrastive Learning with Soft Negatives](https://mlsanity.com/paper/2409.08010/) (2024)
- [Improving Graph Embeddings in Machine Learning Using Knowledge Completion with Validation in a Case Study on COVID-19 Spread](https://mlsanity.com/paper/2511.12071/) (2025)
- [Graph Data Augmentation with Contrastive Learning on Covariate Distribution Shift](https://mlsanity.com/paper/2512.00716/) (2025)

## Abstract (arXiv)

Bipartite graph embedding has recently attracted much attention due to the fact that bipartite graphs are widely used in various application domains. Most previous methods, which adopt random walk-based or reconstruction-based objectives, are typically effective to learn local graph structures. However, the global properties of bipartite graph, including community structures of homogeneous nodes and long-range dependencies of heterogeneous nodes, are not well preserved. In this paper, we propose a bipartite graph embedding called BiGI to capture such global properties by introducing a novel local-global infomax objective. Specifically, BiGI first generates a global representation which is composed of two prototype representations. BiGI then encodes sampled edges as local representations via the proposed subgraph-level attention mechanism. Through maximizing the mutual information between local and global representations, BiGI enables nodes in bipartite graph to be globally relevant. Our model is evaluated on various benchmark datasets for the tasks of top-K recommendation and link prediction. Extensive experiments demonstrate that BiGI achieves consistent and significant improvements over state-of-the-art baselines. Detailed analyses verify the high effectiveness of modeling the global properties of bipartite graph.