Social Network Analysis - From Graph Theory to Applications with Python - Dima Goldenberg - PyCon Israel 2019


Social network analysis is the study of social structures through the use of graph theory. In this talk I will present network theory and application of building and analyzing social networks for practical use-cases in Python with NetworkX.
_________________________________________________________________

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 cover 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.

Code examples for the sessions can be found here: https://github.com/dimgold/pycon_soci...

_____________________________________________________

Dima Goldenberg

I was always excited about looking at patterns, especially in people interactions. This led me to focus on data science and social network research as a career and discover patterns in people behavior. I did my masters research about Influence Maximization in social networks at the Department of Engineering in Tel Aviv University. I love to teach and was teaching different data topics in the academia and outside. Today I am a Data Scientist & Team Lead at Booking.com working on personalization of travel experiences.