Abstract
We address the problem of how social influence affects the spread of information across a population. Existing work has approached such problems through the use of simple models of influence that utilize a single influence mechanism for inducing changes in a population. We have developed a new model of social influence that recognizes and leverages multiple influence mechanisms and multiple types of relations among individuals. Our model increases expressivity and extensibility over that of existing related models and facilitates analysis of influence effects in a multitude of social contexts (e.g., marketing, trends, decision support).
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References
Kempe, D., Kleinberg, J., Tardos, E.: Maximizing the spread of influence through a social network. In: Proceedings of the Ninth International Conference on Knowledge Discovery and Data Mining (KDD), pp. 137–146. ACM Press (2003)
Wicker, A., Doyle, J.: Leveraging Multiple Mechanisms for Information Propagation (in Preparation, 2011)
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Wicker, A.W., Doyle, J. (2012). Leveraging Multiple Mechanisms for Information Propagation. In: Dechesne, F., Hattori, H., ter Mors, A., Such, J.M., Weyns, D., Dignum, F. (eds) Advanced Agent Technology. AAMAS 2011. Lecture Notes in Computer Science(), vol 7068. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27216-5_1
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DOI: https://doi.org/10.1007/978-3-642-27216-5_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-27215-8
Online ISBN: 978-3-642-27216-5
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