Laboratoire de Génie Informatique et d’Automatique de l’Artois

Sabrine MALLEK

Ph.D. student
Member of the research themes:
Contact information:
Evidential k-NN for link prediction
40th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty, ECSQARU'2017, pp 190-200, juillet 2017
Evidential link prediction in uncertain social networks based on node attributes
30th International Conference on Industrial, Engineering other Applications of Applied Intelligent Systems, IEA/AIE’2017, pp 595-601, juin 2017
An evidential method for multi-relational link prediction in uncertain social network
5th international symposium on Integrated Uncertainty in Knowledge Modelling and Decision Making, IUKM'2016, pp 280-292, novembre 2016
Evidential Missing Link Prediction in Uncertain Social Networks
16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016, pp 274-285, Vol. 1, Springer, CCIS, juillet 2016
International conference with review committee
Evidential Link Prediction Based on Group Information
3rd International Conference on Mining Intelligence and Knowledge Exploration, MIKE 2015, pp 482-492, décembre 2015
French conference with review committee
Sabrine MALLEK -- Zied ELOUEDI -- Eric LEFEVRE
Prédiction des liens dans les réseaux sociaux dans le cadre de la thèorie des fonctions de croyance
Actes des 24e rencontres Francophones sur la Logique Floue et ses Applications, LFA 2015, pp 97-104, novembre 2015
International conference with review committee
The Link Prediction Problem Under a Belief Function Framework
27th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2015, Vol. 1014-1020, novembre 2015

Ph.D. topic: "Social network Analysis Under a belief function Framework"


Social networks are large structures that depict social linkage between millions of actors. Social network analysis came out as a tool to study and monitor the patterning of such structures. One of the key problems handled to understand social networks evolving over time is the prediction of a future association between unlinked nodes, known as the link prediction problem. Traditional link prediction methods are designed to deal with social networks under a certain framework. Yet, data of such networks are usually noisy, missing and prone to observation errors causing distortions and likely unaccurate results. The belief function theory is an appealing framework for reasoning under uncertainty that permits to represent, quantify and manage imperfect evidence. The main aim of these works is to build models and approaches to deal with the problem of links prediction under uncertainty which describe the data provided in social network.