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A Survey of Link Prediction in Complex Networks
Abstract
Networks have become increasingly important to model complex systems composed of interacting elements. Network data mining has a large number of applications in many disciplines including protein-protein interaction networks, social networks, transportation networks, and telecommunication networks. Different empirical studies have shown that it is possible to predict new relationships between elements attending to the topology of the network and the properties of its elements. The problem of predicting new relationships in networks is called link prediction. Link prediction aims to infer the behavior of the network link formation process by predicting missed or future relationships based on currently observed connections. It has become an attractive area of study since it allows us to predict how networks will evolve. In this survey, we will review the general-purpose techniques at the heart of the link prediction problem, which can be complemented by domain-specific heuristic methods in practice.
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Autoencoders and their applications in machine learning: a survey
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Artificial intelligence in cancer target identification and drug discovery
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Foundations and Modeling of Dynamic Networks Using Dynamic Graph Neural Networks: A Survey
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Li, H., Sun, Y., Hong, H., Huang, X., Tao, H., Huang, Q., Wang, L., Xu, K., Gan, J., Chen, H., et al. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00469-5 · Cited by 66
Metadata
- Authors
- Martínez, V., Berzal, F., & Cubero, J.-C.
- Venue
- ACM Computing Surveys
- Published
- 2016-12-20
- Volume
- 49
- Issue
- 4
- Pages
- 1-33
- ISSN
- 0360-0300 (ISSN-L)
- 1557-7341
- OpenAlex
- W2564084673
- Cited by
- 695
- References
- 79
- FWCI
- 27.28
- Type
- journal article
- Language
- en
- Open access
- closed
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