Word-of-mouth algorithms: what you don't know will hurt you
Article No.: 2, Pages 1 - 4
Abstract
Word-of-mouth communication has been shown to play a key role in a variety of environments such as viral marketing and churn prediction. A family of algorithms, generally known as information spreading algorithms has been developed to model such pervasive behavior. Although these algorithms have produced good results, in general, they do not consider that the social network reconstructed to model the environment of an individual is limited by the information available. In this paper we study how the missing information (in the form of missing nodes and/or missing links) affects the spread of information in the well-known Dasgupta et al. (2008) algorithm. The results indicate that the error made grows logarithmically with the amount of information (links, nodes or both) unknown.
References
[1]
J. Leskovec et al., 'Sampling from Large Graphs', KDD, 2006.
[2]
K. Dasgupta et al., 'Social Ties and their relevance to churn in mobile telecom network', EDBT, 2008.
[3]
M. Seshadri et al., 'Mobile Call Graphs: Beyond Power-Law and Lognormal Distributions', KDD, 2008.
[4]
J-.P. Onnela et al., 'Structure and tie strenghs in mobile communication networks', New Journal of Physics 9, 2007.
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- Word-of-mouth algorithms: what you don't know will hurt you
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Published In
November 2009
27 pages
ISBN:9781605586946
DOI:10.1145/1641389
- General Chairs:
- James Crowley,
- Yuri Ivanov,
- Christopher Wren,
- Program Chairs:
- Daniel Gatica-Perez,
- Michael Johnston,
- Rainer Stiefelhagen
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Association for Computing Machinery
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Publication History
Published: 06 November 2009
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- Research-article
Conference
ICMI-MLMI '09
Sponsor:
ICMI-MLMI '09: INTERNATIONAL CONFERENCE ON MULTIMODAL INTERFACES/WORKSHOP ON MACHINE LEARNING FOR MULTIMODAL INTERFACES
November 6, 2009
Massachusetts, Cambridge
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