Social Network Analysis: From Graph Theory to Applications with Python
This paper serves as an introductory guide for practitioners and researchers interested in applying social network analysis techniques to various datasets.
This paper provides an overview of social network analysis, starting with graph theory fundamentals and information spread. It then demonstrates practical applications using Python's NetworkX library, including network construction from real datasets, visualization with Matplotlib, social-centrality analysis, and influence maximization.
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.