M2 Data Science - Social and Graph Data Management

The course will teach students the basics of social and graph data management, and is organized in two parts. The first part will study graph metrics (degree distributions, clustering coefficients, distance metrics, etc.) with an objective to apply them to the analysis of real-graph data, especially social network graphs -- as found in Web application such as Facebook or Twitter -- and establish what makes them special compared to standard, random, graphs.

The second part of the course will focus on graph algorithms, as used for graph data analysis (PageRank, probabilistic reachability analysis), and apply them to a variety of applications such as link analysis, influence maximization, or link prediction. This part of the course is focused more on the practical aspect, and will be augmented by practical applications where the concepts will be applied. The course will present well-known systems for graph databases, such as Neo4J.