A survey on bipartite graphs embedding

• Published in: Social Network Analysis and Mining Vol. 13; no. 1; p. 54
• Main Authors: Giamphy, Edward, Guillaume, Jean-Loup, Doucet, Antoine, Sanchis, Kevin
• Format: Journal Article
• Language: English
• Published: Vienna Springer Vienna 21.03.2023; Springer Nature B.V; Springer
• Subjects: Algorithms; Applications of Graph Theory and Complex Networks; Classification; Codes; Computer Science; Data Mining and Knowledge Discovery; Economics; Embedding; Game Theory; Graphical representations; Graphs; Humanities; Information Retrieval; Law; Machine Learning; Methodology of the Social Sciences; Review Paper; Social and Behav. Sciences; Social and Information Networks; Statistics for Social Sciences; Taxonomy; Representation learning; Survey; Machine learning; Benchmark; Graph embeddings; Graph-based pattern representations; Bipartite graph; Data mining
• ISSN: 1869-5450; 1869-5469
• DOI: 10.1007/s13278-023-01058-z

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.