Subventions et des contributions :

Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier. (2017-2018 à 2022-2023)

Increasingly, organizations and communities are turning to big data analytics and social network analysis to make sense of their data, solve computational problems and inform faster and better decisions that might have a business and/or societal impact. Central to the proposed research program is the concept of a social graph - a graph that depicts individuals and relationships between them, similar to a model of an online social network, such as Facebook or Twitter, but the word graph is selected to make a reference to mathematical concept of a graph and emphasize that rigorous mathematical analysis is in place. Graphs are effective representations of pairwise relationships between objects and their analysis can help to better understand the underlying dynamics of large, complex data coming from heterogeneous sources.
The long-term objective of our research is to develop the theory and tools for making the graph mining and exploration process simple and flexible, so it can be easily applied to diverse problem settings and domains. Within this context, a number of short-term objectives are identified. First, an emphasis will be placed on theoretical aspects of a social graph and its properties . This research relates to applications of location-based social search and recommendations , where one might be looking for the best café, restaurant or bar in a city, or the best route to follow to enjoy a city’s points of interests. Moreover, we aim to formalize and introduce algorithmic foundations for group decision making and user credibility assessment in a social graph. This research can help to inform decisions where individuals need to collectively make a choice from a predefined set of alternatives presented to them or to tailor a service or a product to accommodate specific needs of individuals, towards personalization .
The anticipated outcome of our research is twofold: (i) a framework for mining and exploration of large-scale graphs, and (ii) novel algorithms, strategies and tools that can address a range of graph-related problems in social, biological and technological networks. The scientific approach of our research is characterized by acquisition, processing, modeling, analysis and visualization of very large graph data sets and involves the use of advanced techniques for data analysis, such as text analytics, machine learning, predictive analytics, and natural language processing.
Our research program aligns with Canada’s Innovation Agenda . Conducting world-class research in the area of big data and graph analysis and mining has the potential to attract the brightest students from around the world, while keeping domestic talent here. Meanwhile, it will contribute to the training of high quality personnel, with first-rate analytical and problem-solving skills that have the capacity to stimulate knowledge-based companies and boost productivity and innovation taking place across Canada.