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Link prediction in complex networks: A survey
Open accessAbstract
Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.
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Cui, P., Wang, X., Pei, J., & Zhu, W. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2018.2849727 · Cited by 1,274
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Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., & Liu, S. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/d15-1082 · Cited by 657
Metadata
- Authors
- Lü, L., & Zhou, T.
- Venue
- Physica A: Statistical Mechanics and its Applications
- Publisher
- Elsevier BV
- Published
- 2011-03
- Volume
- 390
- Issue
- 6
- Pages
- 1150-1170
- ISSN
- 0378-4371 (ISSN-L)
- OpenAlex
- W1979104937
- Cited by
- 2,819
- References
- 18
- FWCI
- 42.28
- Type
- journal article
- Language
- en
- Open access
- green
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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