  Social network analysis is the process of investigating social structures
through the use of networks and graph theory. It combines a variety of
techniques for analyzing the structure of social networks as well as theories
that aim at explaining the underlying dynamics and patterns observed in these
structures. It is an inherently interdisciplinary field which originally
emerged from the fields of social psychology, statistics and graph theory. This
talk will covers the theory of social network analysis, with a short
introduction to graph theory and information spread. Then we will deep dive
into Python code with NetworkX to get a better understanding of the network
components, followed-up by constructing and implying social networks from real
Pandas and textual datasets. Finally we will go over code examples of practical
use-cases such as visualization with matplotlib, social-centrality analysis and
influence maximization for information spread.
