[Air-L] Large Twitter network graph

[Oluwole Badmus]

Hi Samuel,

Depending on the bus speed/processing capacity of your computer, visualizing large samples can be (obviously) problematic. However, some viz software (such as Gephi, UCNet, Netlyzer, etc.) can be tweaked (settings) to accommodate large node mass visualization. I assumes your best bet is to use these software solely designed for visualization, it helps with computing resource management (a list of useful tools has been compiled by  SocialMedia Lab at Ryerson https://socialmedialab.ca/apps/).

Similarly, check out Deen Freelon’s TSM tools on GitHub ...in case you’re working with Twitter data. It comes in well recommended for preparing node tables for Gephi parsing.

HTH

Best wishes
Martins Olu’ Badmus
LIS PhD Candidate
Western University
Rm 4039 FNB
London, Ontario. Canada N6A 5B9
Skype: olubadmus007

I acknowledge that Western University is located on the traditional lands of the Anishinaabek, Haudenosaunee, Lūnaapéewak and Attawandaron peoples, on lands connected with the London Township and Sombra Treaties of 1796 and the Dish with One Spoon Covenant Wampum. This land continues to be home to diverse Indigenous peoples (e.g. First Nations, Métis and Inuit) whom we recognize as contemporary stewards of the land and vital contributors of our society.

From: Olaniran Psalmuel<mailto:psalmuel35 at gmail.com>
Sent: September 7, 2020 5:14 PM
To: air-l at listserv.aoir.org<mailto:air-l at listserv.aoir.org>
Subject: [Air-L] Large Twitter network graph

Hello all,

I am in need of help and suggestions on a task for my study. I have a dataset with over 1 million nodes that I am trying to graph. My interest is in visualising the degree of connections, centrality and betweenness. Used Networkx module in python but the code for drawing the graph runs for days with no error or output. I have tried out a smaller sample of the dataset and it worked nicely.
Any suggestions on a tool/software that can handle such node size, or an alternative approach would be greatly appreciated.
Thank you in advance.

Samuel Olaniran
PhD Student,
Department of Media Studies,
University of the Witwatersrand
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