A survey on bipartite graphs embedding

Giamphy, Edward (avtor), Guillaume, Jean‑Loup (avtor), Doucet, Antoine (avtor), Sanchis, Kevin (avtor)

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.

• Jezik: Angleški jezik
• Ključne besede: graph embeddings, bipartite graph, representation learning, graph-based pattern representations, machine learning, data mining, survey, benchmark
• Vrsta gradiva: Članek v reviji
• Tipologija: 1.01 - Izvirni znanstveni članek
• Organizacija: FRI - Fakulteta za računalništvo in informatiko
• Status publikacije: Objavljeno
• Različica publikacije: Objavljena publikacija
• Leto izida: 2023
• Št. strani: 16 str.
• Številčenje: Vol. 13, iss. 1, art. 54
• PID: 20.500.12556/RUL-177961
• UDK: 004.89:519.17
• ISSN pri članku: 1869-5469
• DOI: 10.1007/s13278-023-01058-z
• COBISS.SI-ID: 264239619
• Datum objave v RUL: 14.01.2026

Gradivo je del revije

• Naslov: Social network analysis and mining
• Založnik: Springer
• ISSN: 1869-5469
• COBISS.SI-ID: 19185416

Licence

• Licenca: CC BY 4.0, Creative Commons Priznanje avtorstva 4.0 Mednarodna
• Povezava: http://creativecommons.org/licenses/by/4.0/deed.sl
• Opis: To je standardna licenca Creative Commons, ki daje uporabnikom največ možnosti za nadaljnjo uporabo dela, pri čemer morajo navesti avtorja.

Sekundarni jezik

• Jezik: Slovenski jezik
• Ključne besede: vgrajevanje grafov, dvodelni graf, učenje reprezentacij, predstavitve vzorcev na osnovi grafov, strojno učenje, rudarjenje podatkov, anketa, primerjalna analiza