The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Local Probabilistic Models for Link Prediction
Abstract
One of the core tasks in social network analysis is to predict the formation of links (i.e. various types of relationships) over time. Previous research has generally represented the social network in the form of a graph and has leveraged topological and semantic measures of similarity between two nodes to evaluate the probability of link formation. Here we introduce a novel local probabilistic graphical model method that can scale to large graphs to estimate the joint co-occurrence probability of two nodes. Such a probability measure captures information that is not captured by either topological measures or measures of semantic similarity, which are the dominant measures used for link prediction. We demonstrate the effectiveness of the co-occurrence probability feature by using it both in isolation and in combination with other topological and semantic features for predicting co-authorship collaborations on real datasets.
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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
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
Cited by
A Survey of Link Prediction in Complex Networks
Martínez, V., Berzal, F., & Cubero, J.-C. (2017) · ACM Computing Surveys · doi:10.1145/3012704 · Cited by 695
Human mobility, social ties, and link prediction
Wang, D., Pedreschi, D., Song, C., Giannotti, F., & Barabasi, A.-L. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020581 · Cited by 665
New perspectives and methods in link prediction
Lichtenwalter, R. N., Lussier, J. T., & Chawla, N. V. (2010) · Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1835804.1835837 · Cited by 664
A Survey of Link Prediction in Social Networks
Hasan, M. A., & Zaki, M. J. (2011) · Social Network Data Analytics · doi:10.1007/978-1-4419-8462-3_9 · Cited by 566
Simplicial closure and higher-order link prediction
Benson, A. R., Abebe, R., Schaub, M. T., Jadbabaie, A., & Kleinberg, J. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1800683115 · Cited by 548
Metadata
- Authors
- Wang, C., Satuluri, V., & Parthasarathy, S.
- Venue
- Seventh IEEE International Conference on Data Mining (ICDM 2007)
- Publisher
- IEEE
- Published
- 2007-10
- Pages
- 322-331
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2125082260
- Cited by
- 314
- References
- 2
- FWCI
- 5.05
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Wang, C., Satuluri, V., & Parthasarathy, S. (2007). Local Probabilistic Models for Link Prediction. Seventh IEEE International Conference on Data Mining (ICDM 2007). https://doi.org/10.1109/icdm.2007.108
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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.
