Scalable Analytics of Massive Graphs

Massive, complex, interlinked information is collected by scientific research in different spheres of natural and social sciences. Graphs are commonly selected as a model of such information: graphs can successfully represent imprecise, uncertain, noisy data; graphs are well suited for the data structure analysis; graph theory has a well-developed mathematical apparatus forming a solid and sound foundation for graph research. Central to the proposed research are community-based analytics of graphs and diffusion of information through graphs. Research project will investigate which community definitions are best correlated with the existence of influential spreaders among the members of the community, and whether the status (relative importance) of an individual in a community allows him/her become an influential spreader in the larger network. Research into the diffusion of information involves developing the Influence Maximization algorithms that calculate the most influential nodes and/or communities in the graph. TO BE CONT’D

Faculty Supervisor:

Alex Thomo

Student:

Partner:

National Institute of Informatics

Discipline:

Computer science

Sector:

Education

University:

University of Victoria

Program: