A survey on bipartite graphs embedding
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 defi...
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| Published in | Social Network Analysis and Mining Vol. 13; no. 1; p. 54 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
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Vienna
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21.03.2023
Springer Nature B.V Springer |
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| Abstract | 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. |
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| AbstractList | 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. |
| Author | Giamphy, Edward Guillaume, Jean-Loup Doucet, Antoine Sanchis, Kevin |
| Author_xml | – sequence: 1 givenname: Edward surname: Giamphy fullname: Giamphy, Edward email: edward.giamphy@preligens.com organization: AI Research, Preligens, L3i, La Rochelle University – sequence: 2 givenname: Jean-Loup surname: Guillaume fullname: Guillaume, Jean-Loup organization: L3i, La Rochelle University – sequence: 3 givenname: Antoine surname: Doucet fullname: Doucet, Antoine organization: L3i, La Rochelle University – sequence: 4 givenname: Kevin surname: Sanchis fullname: Sanchis, Kevin organization: AI Research, Preligens |
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| Keywords | Representation learning Survey Machine learning Benchmark Graph embeddings Graph-based pattern representations Bipartite graph Data mining |
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| SubjectTerms | 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 |
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| Title | A survey on bipartite graphs embedding |
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