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  • Semantic social network analysis foresees message flows

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    Author(s)
    Cristani, M
    Tomazzoli, C
    Olivieri, F
    Griffith University Author(s)
    Olivieri, Francesco
    Year published
    2016
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    Abstract
    Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph-theoretic measures: degree centrality, closeness centrality and betweeness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, that depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic. We provide an extended model, that is a simplified version of a more general model already documented in the literature, the ...
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    Social Network Analysis is employed widely as a means to compute the probability that a given message flows through a social network. This approach is mainly grounded upon the correct usage of three basic graph-theoretic measures: degree centrality, closeness centrality and betweeness centrality. We show that, in general, those indices are not adapt to foresee the flow of a given message, that depends upon indices based on the sharing of interests and the trust about depth in knowledge of a topic. We provide an extended model, that is a simplified version of a more general model already documented in the literature, the Semantic Social Network Analysis, and show that by means of this model it is possible to exceed the drawbacks of general indices discussed above.
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    Conference Title
    ICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence
    Volume
    1
    DOI
    https://doi.org/10.5220/0005832902960303
    Copyright Statement
    © 2016 by SCITEPRESS – Science and Technology Publications, Lda. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International (CC BY-NC-ND 4.0) License, which permits unrestricted, non-commercial use, distribution and reproduction in any medium, providing that the work is properly cited.
    Publication URI
    http://hdl.handle.net/10072/411971
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    • Conference outputs

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