The advent of big data and availability of information on online social networks has led to great interest in understanding and predicting the behavior of complex social systems. In this course, we will provide an overview of some topics of interest in the analysis of social networks.
We will briefly discuss methods for data acquisition and existing databases, as well as software and techniques for visualizing the connections between individuals. We will describe common methods for describing the topology of complex networks that underlies this data, measuring centrality of individuals, and detecting community structure. Finally, we will give a brief overview of link prediction and recommendation techniques, of interest in the context of many social networks.
Social Networks, Big Data, Centrality, Community Structure, Link Prediction, Classification.
Python, SciPy, Gephi
Linear Algebra, any programming language (Python will be used), familiarity with command line scripting
Dr. Greg Morrison
Statistical Physicist with an interest in complex systems applied to biological, social, and economic systems
Affiliation: IMT School for Advanced Studies, Italy
Dr. Greg Morrison is an Assistant Professor in the Laboratory for Complex Economic Systems at IMT Lucca Institute for Advanced Studies, focusing primarily on the study of complex systems in the context of the dynamics of global innovation networks using patent data, corporate ownership networks, and national input-output systems.
He holds a PhD in physics from the University of Maryland, with a focus on biophysics and statistical mechanics and did his postdoctoral work at Harvard University. His research has spanned two broad but fairly distinct fields: the investigation of problems in single molecule biophysics, using the theoretical approaches of statistical physics, and the study of interconnected meso- or macroscopic systems, using the methods of network theory.
Fields of interests: Statistical Physics, Complex Systems, Network Science, Innovation