23 July 2016
Social Network Analysis
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 Co
This course is targeted to bachelors, masters, PhD students, lecturers and specialists in Computer Science Bioinformatics and Computational Biology, Statistics, Mathematics, Electrical and Computer Engineering, Chemical and Biological Engineering, Industrial Engineering, Material Science and Engineering, Neuroscience, Human-Computer Interaction, Psychology, Business and related discipines
This course is a part of Lviv Computer Science Summer School (whole school's credit cost is 3 ects)
EUR 300: This course is a part of Lviv Computer Science Summer School (whole school's cost is 300 EUR)
EUR 350: This course is a part of Lviv Computer Science Summer School (whole school's cost is 350 EUR)