Consider the decision faced by the user of a social media site of whether or not to accept a friendship request from another user, given the limited amount of information available before deciding. We formalize the problem by defining the expected utility trade-offs derived from the request and simulate the resulting incentives numerically. These incentives provide the basis on which to build social networks determined by the different expectations and preferences of their users. Social networks are generated using a self-organizing map to cluster the decision makers (DMs) by their friendship acceptance behaviour. This behaviour is determined by the distribution of requesters relative to the preferences of the DMs.

Self-Organizing Social Networks by Preference Similarity and the Networking Capacity of their Users / Di Caprio, D.; Santos-Arteaga, F. J.; Tavana, M.. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 83:(2016), pp. 496-503. [10.1016/j.procs.2016.04.241]

Self-Organizing Social Networks by Preference Similarity and the Networking Capacity of their Users

Di Caprio D.;
2016-01-01

Abstract

Consider the decision faced by the user of a social media site of whether or not to accept a friendship request from another user, given the limited amount of information available before deciding. We formalize the problem by defining the expected utility trade-offs derived from the request and simulate the resulting incentives numerically. These incentives provide the basis on which to build social networks determined by the different expectations and preferences of their users. Social networks are generated using a self-organizing map to cluster the decision makers (DMs) by their friendship acceptance behaviour. This behaviour is determined by the distribution of requesters relative to the preferences of the DMs.
2016
Di Caprio, D.; Santos-Arteaga, F. J.; Tavana, M.
Self-Organizing Social Networks by Preference Similarity and the Networking Capacity of their Users / Di Caprio, D.; Santos-Arteaga, F. J.; Tavana, M.. - In: PROCEDIA COMPUTER SCIENCE. - ISSN 1877-0509. - 83:(2016), pp. 496-503. [10.1016/j.procs.2016.04.241]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/250457
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