The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Predicting missing links via local information
Open accessAbstract
AbstractMissing link prediction in networks is of both theoretical interest and practical significance in modern science. In this paper, we empirically investigate a simple framework of link prediction on the basis of node similarity. We compare nine well-known local similarity measures on six real networks. The results indicate that the simplest measure, namely Common Neighbours, has the best overall performance, and the Adamic-Adar index performs second best. A new similarity measure, motivated by the resource allocation process taking place on networks, is proposed and shown to have higher prediction accuracy than common neighbours. It is found that many links are assigned the same scores if only the information of the nearest neighbours is used. We therefore design another new measure exploiting information on the next nearest neighbours, which can remarkably enhance the prediction accuracy.
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Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135
The anatomy of a large-scale hypertextual Web search engine
Brin, S., & Page, L. (1998) · Computer Networks and ISDN Systems · doi:10.1016/s0169-7552(98)00110-x · Cited by 16,004
Hierarchical Organization of Modularity in Metabolic Networks
Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabási, A.-L. (2002) · Science · doi:10.1126/science.1073374 · Cited by 4,224
Hierarchical structure and the prediction of missing links in networks
Clauset, A., Moore, C., & Newman, M. E. J. (2008) · Nature · doi:10.1038/nature06830 · Cited by 2,171
Detecting rich-club ordering in complex networks
Colizza, V., Flammini, A., Serrano, M. A., & Vespignani, A. (2006) · Nature Physics · doi:10.1038/nphys209 · Cited by 1,001
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Reducing Controversy by Connecting Opposing Views
Garimella, K., De Francisci Morales, G., Gionis, A., & Mathioudakis, M. (2017) · Proceedings of the Tenth ACM International Conference on Web Search and Data Mining · doi:10.1145/3018661.3018703 · Cited by 165
Fresh teams are associated with original and multidisciplinary research
Zeng, A., Fan, Y., Di, Z., Wang, Y., & Havlin, S. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01084-x · Cited by 106
Li, Z. (., Fang, X., & Sheng, O. R. L. (2018) · ACM Transactions on Management Information Systems · doi:10.1145/3131782 · Cited by 65
HONGAT: Graph Attention Networks in the Presence of High-Order Neighbors
Zhang, H.-K., Zhang, Y.-G., Zhou, Z., & Li, Y.-F. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i15.29615 · Cited by 14
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Metadata
- Authors
- Zhou, T., Lü, L., & Zhang, Y.-C.
- Venue
- The European Physical Journal B
- Published
- 2009-10
- Volume
- 71
- Issue
- 4
- Pages
- 623-630
- ISSN
- 1434-6028 (ISSN-L)
- 1434-6036
- OpenAlex
- W2007444087
- Cited by
- 1,813
- References
- 5
- FWCI
- 20.15
- Type
- journal article
- Language
- en
- Open access
- green
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