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Authors: Matteo Cristani ; Claudio Tomazzoli and Francesco Olivieri

Affiliation: University of Verona, Italy

Keyword(s): Social Network Analysis, Information Flow, Semantics of Data Flow.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Collaboration and e-Services ; e-Business ; Enterprise Information Systems ; Knowledge Engineering and Ontology Development ; Knowledge-Based Systems ; Semantic Web ; Soft Computing ; Symbolic Systems

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 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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Paper citation in several formats:
Cristani, M. ; Tomazzoli, C. and Olivieri, F. (2016). Semantic Social Network Analysis Foresees Message Flows. In Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART; ISBN 978-989-758-172-4; ISSN 2184-433X, SciTePress, pages 296-303. DOI: 10.5220/0005832902960303

@conference{icaart16,
author={Matteo Cristani and Claudio Tomazzoli and Francesco Olivieri},
title={Semantic Social Network Analysis Foresees Message Flows},
booktitle={Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART},
year={2016},
pages={296-303},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005832902960303},
isbn={978-989-758-172-4},
issn={2184-433X},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART
TI - Semantic Social Network Analysis Foresees Message Flows
SN - 978-989-758-172-4
IS - 2184-433X
AU - Cristani, M.
AU - Tomazzoli, C.
AU - Olivieri, F.
PY - 2016
SP - 296
EP - 303
DO - 10.5220/0005832902960303
PB - SciTePress