A survey on bipartite graphs embedding - Archive ouverte HAL Accéder directement au contenu
Article Dans Une Revue Social Network Analysis and Mining Année : 2023

A survey on bipartite graphs embedding

Résumé

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.
Fichier principal
Vignette du fichier
SNAM - A Survey on Bipartite Graphs Embedding.pdf (984.33 Ko) Télécharger le fichier
Origine : Publication financée par une institution

Dates et versions

hal-04089238 , version 1 (16-05-2023)

Identifiants

Citer

Edward Giamphy, Jean-Loup Guillaume, Antoine Doucet, Kevin Sanchis. A survey on bipartite graphs embedding. Social Network Analysis and Mining, 2023, 13 (1), pp.54. ⟨10.1007/s13278-023-01058-z⟩. ⟨hal-04089238⟩

Collections

L3I UNIV-ROCHELLE
62 Consultations
92 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More