A survey on bipartite graphs embedding
Bibliographic Data
| ID | 4660193 |
|---|---|
| Authors | Edward Giamphy (La Rochelle Université, corresponding author), Jean-Loup Guillaume, Jean‐Loup Guillaume (0000-0002-4615-1563, La Rochelle Université), Antoine Doucet (0000-0001-6160-3356, La Rochelle Université), Kevin Sanchis |
| Year | 2023 |
| Volume | 13 |
| Issue | 1 |
| Publication date | 2023-03-21 |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Social Network Analysis and Mining (JOURNAL) |
| Journal identifiers | ISSN: 1869-5450 • E-ISSN: 1869-5469 |
| Publisher | Springer Science and Business Media LLC (PUBLISHER) |
| DOI | 10.1007/s13278-023-01058-z |
| OpenAlex | W4328051715 |
| Language | EN |
| Citations received | 1 |
| References cited | 37 |
Research on graph representation learning (a.k.a. embedding) has received great attention in recent years and shows effective results for various types of networks. Nevertheless, few initiatives have been focused on the particular case of embeddings for bipartite graphs. In this paper, we first define the graph embedding problem in the case of bipartite graphs. Next, we propose a taxonomy of approaches used to tackle this problem and draw a description of state-of-the-art methods. Then, we establish their pros and cons with respect to conventional network embeddings. Finally, we provide a description of available resources to lead experiments on the subject
Bipartite graph · Embedding · Graph · Graph embedding · Advanced Graph Neural Networks · Complex Network Analysis Techniques · Computer Science · Recommender Systems and Techniques · Artificial Intelligence · Theoretical Computer Science
| Citations per year | 1 |
|---|---|
| Citation span | 2025 - 2025 (1) |
| Highly cited | No |