Abstract
Social networks, which are made of social entities (e.g., individual users) linked by some specific types of interdependencies such as friendship, have become popular to facilitate collaboration and knowledge sharing among users. Such interactions or interdependencies can be dependent on or influenced by user characteristics such as connectivity, centrality, weight, importance, and activity in the networks. As such, some users in the social networks can be considered as highly influential to others. In this article, we propose a computational model that integrates data mining with social computing to help users discover influential friends from a specific portion of the social networks that they are interested in. Moreover, our social network analysis and mining model also allows users to interactively change their mining parameters (e.g., scopes of their interested portions of the social networks).
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This project is partially supported by NSERC (Canada) and University of Manitoba.
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Leung, C.KS., Tanbeer, S.K. & Cameron, J.J. Interactive discovery of influential friends from social networks. Soc. Netw. Anal. Min. 4, 154 (2014). https://doi.org/10.1007/s13278-014-0154-z
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DOI: https://doi.org/10.1007/s13278-014-0154-z