Subventions et des contributions :

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

In many applications, such as social network analysis, fraud detection, and network quality diagnosis, a large amount of complex data are modeled as graphs or networks. Graphs and networks enable us to analyze sophisticated behavior that cannot be understood comprehensively in the past, such as influence propagation and group collaboration in a social network. Such dynamics are a beauty in graph and network data. Moreover, due to the scale and the complexity of data modeled as graphs and networks, changes and evolutions are inevitable. Analyzing and making good use of dynamics in evolving graphs and networks, on the one hand, provide us unprecedented power and tools to conquer big data, and, on the other hand, post grand technical challenges. This proposed research is to embrace the opportunities and address the technical challenges.
The project will investigate a series of novel queries about dynamics in evolving networks and develop effective and efficient algorithms to answer those queries. Moreover, we will develop a graph database to integrate our inventions and support distributed analysis of dynamics in huge evolving networks. Last, we will conduct case studies to assess the algorithmic design and also appraise the effectiveness and feasibility of our research in real applications.
This research is important to academia since it will advance the frontier in the fast-growing area of graph databases. It is also important to industry and applications since the tools developed will bring users critical capability in analyzing dynamics in evolving networks. Moreover, the HQP training component will prepare a group of graduate students for their future professional careers.