Was found an interesting article using Graph Theory in Analysis Social Network. The link for the article – https://towardsdatascience.com/social-network-analysis-from-theory-to-applications-with-python-d12e9a34c2c7
The author provides interesting research with example codes and a conclusion.
Dima Goldenberg’s article is a comprehensive exploration of social network analysis (SNA) combining theoretical underpinnings with practical Python applications. It starts with an introduction to network theory, discussing nodes and edges, and then dives into real-world network characteristics like the Small World phenomenon and Scale-Free networks. The article emphasizes various centrality measures, such as Degree, EigenVector, Closeness, and Betweenness, elucidating their significance in different contexts.
Goldenberg demonstrates network construction and visualization using Python’s NetworkX, exemplified by the Eurovision 2018 votes network. He also explores information diffusion models like the Linear Threshold and Independent Cascade, and tackles the influence maximization problem. The article includes a practical case study from the “Game of Thrones” series, illustrating these concepts with code examples.
The article is well-structured, making it accessible to both beginners and experienced practitioners in data science. Goldenberg’s integration of theoretical concepts with Python-based applications provides a valuable resource for anyone interested in SNA. The use of real-world examples and case studies demonstrates the relevance and application of the theories and techniques discussed. The article effectively showcases the versatility of Python in analyzing and visualizing social networks, making it a go-to guide for SNA enthusiasts.
In conclusion, this article stands out as both a theoretical and practical guide in the field of social network analysis. It skillfully bridges the gap between complex theoretical concepts and their practical application, making it a valuable resource for students, researchers, and professionals interested in network analysis, data science, and related fields.