Jeudi 12 Mai
Heure: 
12:15  13:30 
Lieu: 
Salle B107, bâtiment B, Université de Villetaneuse 
Résumé: 
Agrégation souple et adaptative des graphes hétérogènes avec des attributs hétérogènes 
Description: 
Amine Louati In the enterprise context, people need to exploit, interpret and mainly visualize dierent types of interactions between heterogeneous objects. Graph model is an appropriate way to represent those interactions. Nodes represent the individuals or objects and edges represent the relationships between them. However, extracted graphs are in general heterogeneous (i.e., composed of different node attributes and different relationship types) and large sized which makes it diffcult to visualize and to analyze easily. An adaptive aggregation operation is needed to have more understandable graphs in order to allow users discovering underlyin g information and hidden relationships between objects. Existing graph summarization approaches such as kSNAP are carried out in homogeneous graphs where nodes are described by the same list of attributes that represent only one community. The aim o f this work is to propose a general tool for graph aggregation which addresses both homogeneous and heterogeneous graphs. To do that, we develop a new soft and adaptive approach to aggregate heterogeneous graphs using the definition of Rough Set Theory (RST) combined with Formal Concept Analysis (FCA), the well known KMedoids and the hierarchical clustering methods. Aggregated graphs are produced according to userselected node attributes and relationships. To evaluate the quality of the obtained summaries, we propose two quality measures that evaluate respectively the similarity and the separability of groups based on the notion of common neighbor nodes. Experimental results demonstrate that our approach is effective for its ability to produce a high quality solution with relevant interpretations. 

