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The link prediction problem for social networks
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Source Conference on Information and Knowledge Management archive
Proceedings of the twelfth international conference on Information and knowledge management table of contents
New Orleans, LA, USA
SESSION: Poster papers - short papers table of contents
Pages: 556 - 559  
Year of Publication: 2003
ISBN:1-58113-723-0
Authors
David Liben-Nowell  Massachusetts Institute of Technology
Jon Kleinberg  Cornell University
Sponsors
ACM: Association for Computing Machinery
SIGMIS: ACM Special Interest Group on Management Information Systems
SIGIR: ACM Special Interest Group on Information Retrieval
Publisher
ACM  New York, NY, USA
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Downloads (6 Weeks): 78,   Downloads (12 Months): 487,   Citation Count: 44
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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 develop approaches to link prediction based on measures 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.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

 
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CITED BY  44

Collaborative Colleagues:
David Liben-Nowell: colleagues
Jon Kleinberg: colleagues