Conference Proceeding Article
Link structures are important patterns one looks out for when modeling and analyzing social networks. In this paper, we propose the task of mining interesting Link Formation rules (LF-rules) containing link structures known as Link Formation patterns (LF-patterns). LF-patterns capture various dyadic and/or triadic structures among groups of nodes, while LF-rules capture the formation of a new link from a focal node to another node as a postcondition of existing connections between the two nodes. We devise a novel LF-rule mining algorithm, known as LFR-Miner, based on frequent subgraph mining for our task. In addition to using a support-confidence framework for measuring the frequency and signi¯cance of LF-rules, we introduce the notion of expected support to account for the extent to which LF- rules exist in a social network by chance. Specifically, only LF-rules with higher-than-expected support are considered interesting. We conduct empirical studies on two real-world social networks, namely Epinions and myGamma. We report interesting LF-rules mined from the two networks, and compare our findings with earlier findings in social network analysis.
Social network analysis, Local structures, Frequent subgraph mining, social networks
Databases and Information Systems | Numerical Analysis and Scientific Computing | Social Media
Data Management and Analytics
CIKM'10: Proceedings of the 19th ACM International Conference on Information and Knowledge Management: October 26-30, 2010, Toronto
City or Country
LEUNG, Cane Wing-Ki; LIM, Ee Peng; LO, David; and WENG, Jianshu.
Mining Interesting Link Formation Rules in Social Networks. (2010). CIKM'10: Proceedings of the 19th ACM International Conference on Information and Knowledge Management: October 26-30, 2010, Toronto. 209-218. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/624
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