Intro to Social Network Analysis (SNA) and Visualization | EdisonX Academy

Intro to Social Network Analysis (SNA) and Visualization

Social networks are ubiquitous in the world we live in. From protein and neural networks in our body to online social networks like Facebook and Twitter, the analysis of networks is an emerging area of great importance. In this talk, we look at the fundamental tenets of social network analysis, and some associated visualisations using Gephi.


Thanks to the advent of big data in various domains, like social media and social network sites, the study of social networks using computational methods has gained popularity. In this introductory talk, I will introduce some fundamental notations and properties that are used to characterize such networks, and discuss a number of key network measures that are commonly used in data science contexts. We will also learn how a social network and its associated metrics can be generated and visualized using Gephi, an open-source network visualization and analysis tool that offers a set of user-friendly, yet powerful, features for performing SNA.

This will be a hands-on walkthrough session, we recommend you to install the "Gephi" Software and also have the sample dataset with you before the session.

Software to install: Click here to download

Sample GML File: Click here to download

Sample Description: Coauthorships in network science: coauthorship network of scientists working on network theory and experiment, as compiled by M. Newman in May 2006. A figure depicting the largest component of this network can be found here. M. E. J. Newman, Phys. Rev. E 74, 036104 (2006).

Other Sample Dataset(s): Click here to view

Who should Attend?

This talk is aimed at students and professionals working in any data-related area that uses quantitative approaches. It will equip you with the basic knowledge and skills to understand, conceptualize, analyze and visualize network data.

Note: Anyone with a general interest in social networks. Some prior knowledge in graph theory is an added plus. Also, some basic exposure to R and Python is helpful to fully appreciate the benefit of the SNA libraries and packages that I would briefly mention in my talk.

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