The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
The link‐prediction problem for social networks
Abstract
Given a snapshot of a social network, can we infer which new interactions among its members are likely to occur in the near future? We formalize this question as the link‐prediction problem, and we develop approaches to link prediction based on measures for analyzing the “proximity” of nodes in a network. Experiments on large coauthorship networks suggest that information about future interactions can be extracted from network topology alone, and that fairly subtle measures for detecting node proximity can outperform more direct measures.
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Cites
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
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Metadata
- Authors
- Liben‐Nowell, D., & Kleinberg, J.
- Venue
- Journal of the American Society for Information Science and Technology
- Publisher
- Wiley
- Published
- 2007-03-26
- Volume
- 58
- Issue
- 7
- Pages
- 1019-1031
- ISSN
- 1532-2882 (ISSN-L)
- 1532-2890
- OpenAlex
- W4232932184
- Cited by
- 3,043
- References
- 2
- FWCI
- 20.01
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
