# Learning Vertex Representations for Bipartite Networks

arXiv: https://arxiv.org/abs/1901.09676 · HTML: https://mlsanity.com/paper/1901.09676/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2019.

## Built on by (most cited first, 9 of 9)

- [Multiplex Bipartite Network Embedding using Dual Hypergraph Convolutional Networks](https://mlsanity.com/paper/2102.06371/) (2021)
- [Distilled Large Language Model-Driven Dynamic Sparse Expert Activation Mechanism](https://mlsanity.com/paper/2603.26735/) (2026)
- [Unification of Closed-Open Industrial Detection Scenarios: New Large-Scale Benchmarks,Challenges and Baselines](https://mlsanity.com/paper/2606.07953/) (2026)
- [KFTD: Koopman-Fourier Time-Differentiable Network for Continuous Ocean Spatiotemporal Forecasting](https://mlsanity.com/paper/2606.17070/) (2026)
- [DeHIN: A Decentralized Framework for Embedding Large-scale Heterogeneous Information Networks](https://mlsanity.com/paper/2201.02757/) (2022)
- [KAN-FIF: Spline-Parameterized Lightweight Physics-based Tropical Cyclone Estimation on Meteorological Satellite](https://mlsanity.com/paper/2602.12117/) (2026)
- [FMRFusion: Frequency-Aware Multi-View Representation Learning for Heterogeneous Image Fusion](https://mlsanity.com/paper/2606.07985/) (2026)
- [Content-Induced Spatial-Spectral Aggregation Network for Change Detection in Remote Sensing Images](https://mlsanity.com/paper/2606.10328/) (2026)
- [Building Change Detection in Earthquake: A Multi-Scale Interaction Network and A Change Detection Dataset](https://mlsanity.com/paper/2606.10329/) (2026)

## Abstract (arXiv)

Recent years have witnessed a widespread increase of interest in network representation learning (NRL). By far most research efforts have focused on NRL for homogeneous networks like social networks where vertices are of the same type, or heterogeneous networks like knowledge graphs where vertices (and/or edges) are of different types. There has been relatively little research dedicated to NRL for bipartite networks. Arguably, generic network embedding methods like node2vec and LINE can also be applied to learn vertex embeddings for bipartite networks by ignoring the vertex type information. However, these methods are suboptimal in doing so, since real-world bipartite networks concern the relationship between two types of entities, which usually exhibit different properties and patterns from other types of network data. For example, E-Commerce recommender systems need to capture the collaborative filtering patterns between customers and products, and search engines need to consider the matching signals between queries and webpages.