# Social Network Analysis: From Graph Theory to Applications with Python

arXiv: https://arxiv.org/abs/2102.10014 · HTML: https://mlsanity.com/paper/2102.10014/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 19 references that are in the map.
- First posted on arXiv 5 Feb 2021.

## Builds on (most cited first, 1 of 1)

- [A Scalable Heuristic for Viral Marketing Under the Tipping Model](https://mlsanity.com/paper/1309.2963/) (2013)

## Built on by (most cited first, 4 of 4)

- [Overcoming Pitfalls in Graph Contrastive Learning Evaluation: Toward Comprehensive Benchmarks](https://mlsanity.com/paper/2402.15680/) (2024)
- [Enhancing Graph Contrastive Learning with Node Similarity](https://mlsanity.com/paper/2208.06743/) (2022)
- [Shapley-Guided Utility Learning for Effective Graph Inference Data Valuation](https://mlsanity.com/paper/2503.18195/) (2025)
- [Forecasting Graph Signals with Recursive MIMO Graph Filters](https://mlsanity.com/paper/2210.15258/) (2022)

## Abstract (arXiv)

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.