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Tshimula, Jean-Marie; Chikhaoui, Belkacem et Wang, Shengrui (sous presse). HAR-search: A Method to Discover Hidden Affinity Relationships in Online Communities. Dans The 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2019). Vancouver, Canada : IEEE/ACM.
Fichier(s) associé(s) à ce document :|
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- HARsearchASONAM2019-up.pdf
Contenu du fichier : Manuscrit accepté (révisé après évaluation) Accès restreint |
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| Catégorie de document : | Communications dans des actes de congrès/colloques |
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| Évaluation par un comité de lecture : | Oui |
| Étape de publication : | Accepté (sous presse) |
| Résumé : | This paper addresses the problem of discovering hidden affinity relationships in online communities. Online discussions assemble people to talk about various types of topics and to share information. People progressively develop the affinity, and they get closer as frequently as they mention themselves in messages and they send positive messages to one another. We propose an algorithm, named HAR-search, for discovering hidden affinity relationships between individuals. Based on Markov Chain Models, we derive the affinity scores amongst individuals in an online community. We show that our method allows to track the evolution of the affinity over time and to predict affinity relationships arisen from the influence of certain community members. The comparison with the state-of-the-art method shows that our method results in robust discovery and considers minute details. |
| Déposant: | Chikhaoui, Belkacem |
| Responsable : | Belkacem Chikhaoui |
| Dépôt : | 28 juin 2019 15:24 |
| Dernière modification : | 25 oct. 2019 17:38 |
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