Show simple item record

dc.contributor.authorCristani, M
dc.contributor.authorTomazzoli, C
dc.contributor.authorOlivieri, F
dc.date.accessioned2022-02-03T05:35:25Z
dc.date.available2022-02-03T05:35:25Z
dc.date.issued2016
dc.identifier.isbn9789897581724
dc.identifier.doi10.5220/0005832902960303
dc.identifier.urihttp://hdl.handle.net/10072/411971
dc.description.abstractSocial 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.
dc.publisherSCITEPRESS - Science and and Technology Publications
dc.relation.ispartofconferencename8th International Conference on Agents and Artificial Intelligence
dc.relation.ispartofconferencetitleICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence
dc.relation.ispartofdatefrom2016-02-24
dc.relation.ispartofdateto2016-02-26
dc.relation.ispartofpagefrom296
dc.relation.ispartofpageto303
dc.relation.ispartofvolume1
dc.titleSemantic social network analysis foresees message flows
dc.typeConference output
dcterms.bibliographicCitationCristani, M; Tomazzoli, C; Olivieri, F, Semantic social network analysis foresees message flows, ICAART 2016 - Proceedings of the 8th International Conference on Agents and Artificial Intelligence, 2016, 1, pp. 296-303
dcterms.licensehttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.date.updated2022-02-03T05:32:18Z
dc.description.versionVersion of Record (VoR)en_US
gro.rights.copyright© 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.en_US
gro.hasfulltextFull Text
gro.griffith.authorOlivieri, Francesco


Files in this item

This item appears in the following Collection(s)

  • Conference outputs
    Contains papers delivered by Griffith authors at national and international conferences.

Show simple item record