Articles de journaux |
Lamya Benamar; Christine Balagué; Mohamad Ghassany The Identification and Influence of Social Roles in a Social Media Product Community Article de journal Journal of Computer‐Mediated Communication, 2017, ISSN: 1083-6101. Résumé | Liens | BibTeX @article{benamar2017the,
title = {The Identification and Influence of Social Roles in a Social Media Product Community},
author = {Lamya Benamar and Christine Balagué and Mohamad Ghassany},
url = {http:https://doi.org/10.1111/jcc4.12195},
doi = {10.1111/jcc4.12195},
issn = {1083-6101},
year = {2017},
date = {2017-01-01},
journal = {Journal of Computer‐Mediated Communication},
publisher = {Wiley Online Library},
abstract = {This research focuses on the identification of social roles and an investigation of their influence in online context. Relying on a systemic approach for role conceptualization, we investigate member's activity, shared content and position in the network within a consumer to consumer social media-based community (SMC) around a product. This investigation led to the identification of ten core roles, based on three key elements: object of interest (product, practice, and community), main contribution type (sharing information and seeking information), individual orientation (factual, emotional). We propose an explanation about how these roles, through their positioning, participate in the community dynamics and how they contribute to the creation and diffusion of cookery as a social practice, shaping the periphery around this practice.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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This research focuses on the identification of social roles and an investigation of their influence in online context. Relying on a systemic approach for role conceptualization, we investigate member's activity, shared content and position in the network within a consumer to consumer social media-based community (SMC) around a product. This investigation led to the identification of ten core roles, based on three key elements: object of interest (product, practice, and community), main contribution type (sharing information and seeking information), individual orientation (factual, emotional). We propose an explanation about how these roles, through their positioning, participate in the community dynamics and how they contribute to the creation and diffusion of cookery as a social practice, shaping the periphery around this practice. |
Mohamad Ghassany; Younès Bennani Collaborative Fuzzy Clustering of Variational Bayesian Generative Topographic Mapping Article de journal International Journal of Computational Intelligence and Applications, 14 , 2015. Résumé | Liens | BibTeX @article{ijcia14,
title = {Collaborative Fuzzy Clustering of Variational Bayesian Generative Topographic Mapping},
author = {Mohamad Ghassany and Younès Bennani},
url = {http://www.worldscientific.com/doi/abs/10.1142/S1469026815500017},
year = {2015},
date = {2015-01-01},
journal = {International Journal of Computational Intelligence and Applications},
volume = {14},
abstract = {In this paper, we propose a Collaborative Clustering method based on Variational Bayesian Generative Topographic Mapping (VBGTM). To do so, we first propose a method that combines VBGTM and Fuzzy c-means (FCM). Collaborative clustering is useful to achieve interaction between different sources of information for the purpose of revealing underlying structures and regularities within data sets. It can be treated as a process of consensus building where we attempt to reveal a structure that is common across all sets of data. VBGTM was introduced as a variational approximation of Generative Topographic Mapping (GTM) to control data overfitting. It provides an analytical approximation to the posterior probability of the latent variables and the distribution of the input data in the latent space. It can be effectively applied to visualize and explore properties of the data. But when the number of latent points is large, similar units need to be grouped (i.e., clustered) to facilitate quantitative analysis of the map and the data. We use FCM to determine the prototypes as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from VBGTM. So, by combining the two algorithms, we develop a method that can do visualization and clustering at the same time. We observe that the hybrid method (F-VBGTM) performs very well in terms of many cluster-validity indexes.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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In this paper, we propose a Collaborative Clustering method based on Variational Bayesian Generative Topographic Mapping (VBGTM). To do so, we first propose a method that combines VBGTM and Fuzzy c-means (FCM). Collaborative clustering is useful to achieve interaction between different sources of information for the purpose of revealing underlying structures and regularities within data sets. It can be treated as a process of consensus building where we attempt to reveal a structure that is common across all sets of data. VBGTM was introduced as a variational approximation of Generative Topographic Mapping (GTM) to control data overfitting. It provides an analytical approximation to the posterior probability of the latent variables and the distribution of the input data in the latent space. It can be effectively applied to visualize and explore properties of the data. But when the number of latent points is large, similar units need to be grouped (i.e., clustered) to facilitate quantitative analysis of the map and the data. We use FCM to determine the prototypes as well as the resultant clusters and the corresponding membership functions of the input data, based on the latent variables obtained from VBGTM. So, by combining the two algorithms, we develop a method that can do visualization and clustering at the same time. We observe that the hybrid method (F-VBGTM) performs very well in terms of many cluster-validity indexes. |
Mohamad Ghassany; Nistor Grozavu; Younes Bennani Collaborative Clustering Using Prototype-Based Techniques Article de journal International Journal of Computational Intelligence and Applications, 11 , 2012. Résumé | Liens | BibTeX @article{Ghassany12-journal,
title = {Collaborative Clustering Using Prototype-Based Techniques},
author = {Mohamad Ghassany and Nistor Grozavu and Younes Bennani},
url = {http://www.worldscientific.com/doi/abs/10.1142/S1469026812500174},
year = {2012},
date = {2012-01-01},
journal = {International Journal of Computational Intelligence and Applications},
volume = {11},
abstract = {The aim of collaborative clustering is to reveal the common structure of data distributed on different sites. In this paper, we present a formalism of topological collaborative clustering using prototype-based clustering techniques; in particular we formulate our approach using Kohonen's Self-Organizing Maps. Maps representing different sites could collaborate without recourse to the original data, preserving their privacy. We present two different approaches of collaborative clustering: horizontal and vertical. The strength of collaboration (confidence exchange) between each pair of datasets is determined by a parameter, we call coefficient of collaboration, to be estimated iteratively during the collaboration phase using a gradient-based optimization, for both the approaches. The proposed approaches have been validated on several datasets and experimental results have shown very promising performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
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The aim of collaborative clustering is to reveal the common structure of data distributed on different sites. In this paper, we present a formalism of topological collaborative clustering using prototype-based clustering techniques; in particular we formulate our approach using Kohonen's Self-Organizing Maps. Maps representing different sites could collaborate without recourse to the original data, preserving their privacy. We present two different approaches of collaborative clustering: horizontal and vertical. The strength of collaboration (confidence exchange) between each pair of datasets is determined by a parameter, we call coefficient of collaboration, to be estimated iteratively during the collaboration phase using a gradient-based optimization, for both the approaches. The proposed approaches have been validated on several datasets and experimental results have shown very promising performance. |
Conférences |
Lamya Benamar; Mohamad Ghassany; Christine Balagu 'e Analyzing interactions and identifying social roles in a brand community on social networks 5500 - 5599 Conférence The European Marketing Academy, EMAC2016., 2016. BibTeX @conference{Benamar16-emac,
title = {Analyzing interactions and identifying social roles in a brand community on social networks},
author = {Lamya Benamar and Mohamad Ghassany and Christine Balagu 'e},
year = {2016},
date = {2016-01-01},
booktitle = {The European Marketing Academy, EMAC2016.},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Mohamad Ghassany; Nistor Grozavu; Younes Bennani Collaborative Multi-View Clustering 5500 - 5599 Conférence The 2013 International Joint Conference on Neural Networks, IJCNN13, Dallas, USA, 2013. BibTeX @conference{Ghassany-ijcnn13,
title = {Collaborative Multi-View Clustering},
author = {Mohamad Ghassany and Nistor Grozavu and Younes Bennani},
year = {2013},
date = {2013-01-01},
booktitle = {The 2013 International Joint Conference on Neural Networks, IJCNN13, Dallas, USA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Mohamad Ghassany; Nistor Grozavu; Younes Bennani Collaborative Generative Topographic Mapping 5500 - 5599 Conférence Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar, Lecture Notes in Computer Science 2012. BibTeX @conference{Ghassany12-iconip,
title = {Collaborative Generative Topographic Mapping},
author = {Mohamad Ghassany and Nistor Grozavu and Younes Bennani},
year = {2012},
date = {2012-01-01},
booktitle = {Neural Information Processing: 19th International Conference, ICONIP 2012, Doha, Qatar},
series = {Lecture Notes in Computer Science},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Nistor Grozavu; Mohamad Ghassany; Younes Bennani Learning confidence exchange in Collaborative Clustering 5500 - 5599 Conférence The 2011 International Joint Conference on Neural Networks, IJCNN11, San Jose, CA, USA, 2011. BibTeX @conference{Grozavu2011,
title = {Learning confidence exchange in Collaborative Clustering},
author = {Nistor Grozavu and Mohamad Ghassany and Younes Bennani},
year = {2011},
date = {2011-01-01},
booktitle = {The 2011 International Joint Conference on Neural Networks, IJCNN11, San Jose, CA, USA},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Nistor Grozavu; Mohamad Ghassany; Youn`es Bennani Apprentissage de la confiance des échanges en classification collaborative non supervisée 5500 - 5599 Conférence Editions Publibook, 2011. BibTeX @conference{grozavu2011apprentissage,
title = {Apprentissage de la confiance des échanges en classification collaborative non supervisée},
author = {Nistor Grozavu and Mohamad Ghassany and Youn{`e}s Bennani},
year = {2011},
date = {2011-01-01},
journal = {7e Plateforme AFIA, Association Française pour l'Intelligence Artificielle, Chamb'ery, 16 au 20 mai 2011},
pages = {217},
publisher = {Editions Publibook},
keywords = {},
pubstate = {published},
tppubtype = {conference}
}
|
Thèses |
Mohamad Ghassany Contributions to Collaborative Clustering Thèse de doctorat 2013. BibTeX @phdthesis{mathese,
title = {Contributions to Collaborative Clustering},
author = {Mohamad Ghassany},
year = {2013},
date = {2013-01-01},
keywords = {},
pubstate = {published},
tppubtype = {phdthesis}
}
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