https://www.youtube.com/watch?v=px7ff2_Jeqw

This repository contains social network analyses code examples for PyCon 2019 talk.

• Eurovision Song Contest 2018 votes network visualization
• Information spread and Influence maximization on Game of Thrones network

Talk recap:

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 and social marketing.

If you wish to cite this resource in your academic research, please use the following format:

Goldenberg, Dmitri. “Social network analysis: From graph theory to applications with python.” PyCon 2019 — 3rd Israeli National Python Conference, Israel, 2019. arXiv preprint arXiv:2102.10014 (2021).

Credits:

• Game of thrones dataset @jeffreylancaster
• Networks tutorial @MridulS
• Flags images @linssen
• Eurovision Data

Papers:

• Timing matters: Influence Maximization in Social Networks Through Scheduled Seeding - D. Goldenberg et al.
• Active viral marketing: Incorporating continuous active seeding efforts into the diffusion model - A. Sela et al.
• Maximizing the spread of influence through a social network - E. Tardos et al.
• Efficient influence maximization in social networks - W. Chen et al.