Chercheur·e du réseau

Faculté des sciences
Mohamed Bouguessa
Université du Québec à Montréal (UQÀM)
Faculté des sciences
Département d'informatique
Intérêts de recherche
  • Big Data
  • Apprentissage automatique
  • Analyse des réseaux sociaux
  • Forage de données
Informations générales
Numéro de téléphone : 
(514) 987-3000 x5541
Numéro de local : 
Principales réalisations
Multidimensional Heterogeneous Information Network Analysis and Mining

This research program proposes to address several fundamental problems related to the analysis of massive information networks. Our long-term goal is to develop theories and algorithms for mining knowledge from large-scale time-evolving heterogeneous and multidimensional information networks, that is, networks that consist of multi-typed nodes (entities) and multiple edges (relations) between the network nodes. As driven application, we consider the analysis of large-scale social networks in order to provide better understanding of human-Web interactions and give semantic interpretation and insight into the various roles and groupings in online social networks.

NSREC Discovery Grant : 2018-2024

Data Mining for Energy Analysis

This project is conducted in collaboration with a major multinational corporation. The general goal is to use data mining techniques in order to analyse massive data collected from smart meters and energy monitoring system. The aim is to extract useful patterns of actions that offer insight to track and analyze customers’ energy consumption over different time stamps and develop an automatic mechanism to understand consumption trends and provide recommendations for better energy use, achieve energy efficiency goals and improve business operations.

Industrial project.

Social Networks Analysis

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

Selected publications:

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.

Learning Models for Time Evolving Networked Data

Todays' networked data are dynamic and change continually over time. Recently, a few methods have been proposed in an attempt to solve this problem. Nevertheless, the vast majority of existing methods encounter difficulties to handle large networks observed over long time periods. There are many open questions that have yet been addressed. In particular, what is the critical time moment in which groups’ membership changes most? What are the most important features that affect community membership over short and long time period? Furthermore, in some situations the whole network is not available at one time, but available in the form of continuous stream (e.g. components of the network are received more or less continuously as time progress). This goal of this project is to create analytical tools to track and predict structural changes and explain the evolution of the network over time. The devised approaches should be able to analytically model the co-evolution of multi-typed entities and the relationships among them.

Selected publications:

Étienne Gael Tajeuna, Mohamed Bouguessa and Shengrui Wang, "Survival analysis for modeling critical events that communities may undergo in dynamic social networks ", The 32nd ACM Symposium on Applied Computing, (ACM SAC 2017),  pp. 1068-1075, 2017.

Étienne Gael Tajeuna, Mohamed Bouguessa and Shengrui Wang, "Tracking the evolution of community structures in time-evolving social networks", ACM/IEEE International Conference on Data Science and Advanced Analytics (DSAA 2015), pp. 1-10, 2015.


Clustering Large High Dimensional Data


Mohamed Bouguessa,“Clustering Categorical Data in Projected Spaces”, Data Mining and Knowledge Discovery,. vol. 29, no 1, pp. 3-38, 2015.