Title: The Social Network
Abstract: In January 2021, I moved to Vancouver, Canada to pursue my Masters at Emily Carr University of Art + Design. It was the peak of the pandemic and all public places were shut including educational institutions. I had already completed the first 4-months of my program in India and met all of my course mates and instructors on zoom. We would meet each other everyday during our online classes on small screens and often talked about the fact that how crazy it would be to meet each other in-person for the first time. Being on zoom all of us looked as if we were of the same height and once in a while when someone shared their picture during a class presentation... it would come as a surprise of how tall or short someone was.

Fast-forward to May 2021, I was in the early stages of my research and just introducing myself to Data Visualization. The idea was to use data visualization as a tool to make large amounts of data comprehensible by the audience... in other other words finding out if visualizing data could be a better way to send across a message. During this time I was reading the book 'Dear Data' by Giorgia Lupi and Stephanie Posavec. In one of their chapters the authors have written about how 
'data is not just about numbers and percentages, but can also be presented in its lateral truths that can reveal emotions, wants, needs and personality of the person it belongs to'.

This got me thinking of designing an experiment of visualizing a network of the people from my cohort that I met for the first time in-person after coming to Vancouver. Apart from the names of these people, I was also documenting other data like:
1. Where did I first meet them? (campus/not on campus)
2. Were they students or Faculty/Staff?
3. Were they from my program?
4. Were they males or females?
5. Were they local or international?
Conclusion: 'Any network visualization, which is the visualization of relationships (edges or links) between data elements or nodes, in its Macro view should provide a bird's eye view into the network and highlight certain clusters as well as isolated groups within its structures' (Lima M., 2013).

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