Zoo Guide to Network Embedding
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866912727035805696 |
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| author | Baptista, Anthony Sánchez-García, Rubén J. Baudot, Anaïs Bianconi, Ginestra |
| author_facet | Baptista, Anthony Sánchez-García, Rubén J. Baudot, Anaïs Bianconi, Ginestra |
| contents | Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted lots of interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_03474 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Zoo Guide to Network Embedding Baptista, Anthony Sánchez-García, Rubén J. Baudot, Anaïs Bianconi, Ginestra Social and Information Networks Machine Learning Mathematical Physics Networks have provided extremely successful models of data and complex systems. Yet, as combinatorial objects, networks do not have in general intrinsic coordinates and do not typically lie in an ambient space. The process of assigning an embedding space to a network has attracted lots of interest in the past few decades, and has been efficiently applied to fundamental problems in network inference, such as link prediction, node classification, and community detection. In this review, we provide a user-friendly guide to the network embedding literature and current trends in this field which will allow the reader to navigate through the complex landscape of methods and approaches emerging from the vibrant research activity on these subjects. |
| title | Zoo Guide to Network Embedding |
| topic | Social and Information Networks Machine Learning Mathematical Physics |
| url | https://arxiv.org/abs/2305.03474 |