RESEARCH METHODOLOGY : Social Network Analysis with Python

I’m using social network analysis as a proof of concept for my hypothesis on “Stakeholder Engagement in Decision-Making for Smart-City”. My approach of academic research in urban science consists in exploiting the Open-source Intelligence underlying in digital social interactions.

Network analysis comes from the graph theory and has captivating applications in the digital world, as the study of connection between things. Practically speaking, any network is composed of nodes and links. It can take unlimited variation of shapes and sizes and can be fulfilled with additional properties, represented with directions, weights, clusters and loops.
The idea of learning effective representations from raw data using Artificial Neural Networks has been employed in numerous Machine Learning domains such as Computer Vision and Natural Language Processing.

My academic research problematic is a globalized issue for future cities and societies through the advent of civic technologies, which I aim to find a solution by comparing culturally far case study cities in Asia, Middle-East and North-Eastern Europe. Applying the exactly same protocol of data collection, data analysis and data visualization, will allow to display the common patterns between cases and the diverging cultural points in the way to use technologies for human societies.

=> To mention this article “Research Methodology : Social Network Analysis with Python” Julien Carbonnell, 2020…