The increasing amount of communication between individuals in e-formats (e.g. email, instant messaging, blogs, etc.) has motivated computational research in social network analysis. Social network analysis techniques aim to search communities of shared interests or leaders within communities. Social networks are often represented as graphs, where nodes represent individuals and edges represent the relationship between them. Such graphs are massive, in which node may contain a large amount of text data. The goal of this project is to make social network analysis more effective is to develop techniques that take into account textual content, uncertainty, incompleteness, heterogeneity of data sources and the need of developing specialized algorithms for Web applications that involve continuous stream of edges
Oualid Boutemine and Mohamed Bouguessa, “Mining Community Structures in Multidimensional Networks”, ACM Transaction on Knowledge Discovery from Data. vol. 11, no 4. Article 51, 36 pages, 2017.
Amani Chouchane and Mohamed Bouguessa, "Identifying Anomalous Nodes in Multidimensional Networks", ACM/IEEE International Conference on Data Science and Advanced Analytics (DSAA 2017), pp. 601-610, 2017.