The DOI matches its OpenAlex 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 co-authorship 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
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
Estimating the Support of a High-Dimensional Distribution
Schölkopf, B., Platt, J. C., Shawe-Taylor, J., Smola, A. J., & Williamson, R. C. (2001) · Neural Computation · doi:10.1162/089976601750264965 · Cited by 6,168
Cited by
De-anonymizing Social Networks
Narayanan, A., & Shmatikov, V. (2009) · 2009 30th IEEE Symposium on Security and Privacy · doi:10.1109/sp.2009.22 · Cited by 1,301
A Survey of Statistical Network Models
Goldenberg, A., Zheng, A. X., Fienberg, S. E., & Airoldi, E. M. (2010) · Foundations and Trends® in Machine Learning · doi:10.1561/2200000005 · Cited by 819
Metadata
- Authors
- Liben‐Nowell, D., & Kleinberg, J. M.
- Venue
- ACM International Conference on Information and Knowledge Management (CIKM)
- Published
- 2003-01-01
- OpenAlex
- W4252435334
- Cited by
- 174
- References
- 2
- FWCI
- 1.25
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Liben‐Nowell, D., & Kleinberg, J. M. (2003). The link prediction problem for social networks. ACM International Conference on Information and Knowledge Management (CIKM). https://doi.org/10.1145/956958.956972
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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.
