Note: All the code for this project (Python) can be found, along with the graphs embedded in the blog on my Github repository. The code is licensed under Apache 2.0 so you’re free to use it for commercial purposes. My GitHub repo is here.

Network science has grown in popularity in the last few decades, especially with the ubiquity of social media platforms and the internet in general. However, much remains to be explored and discovered in relation to the processes by which networks form, the underlying social forces that underpin their formation, and thus the statistical distributions formed by those social forces.

In this semi-formal blog/paper I [...]

In a recent project that I’ve been working on (Twitter SNA focus and I’ll tell you more about this project later), an interesting question came up from the client – “So this group is always retweeting/sharing from this group, so they must know each other and/or be ‘organized’, correct?” Well, not so much and here are some reasons why:

First, we have to get a fundamental understanding of what it means to retweet/reshare/like a message and to keep things simple, we’ll focus on twitter’s social media network.

The traditional view is that you would only retweet messages that you either agree with or support, but that [...]

A few years ago a company named Klout appeared that promised to be able to measure social media influence using what they called the Klout score. Today Klout was bought out by some marketing automation company called Lithium Tech.

When I hear of Klout, I was so surprised that someone had finally and quickly solved the never-ending challenge of measuring and ranking social influence through a mathematical algorithm, so I jumped right into it, created an account and signed up to have my score measured.

Only to find out that the scores were nonsensical, the automated topic-choosing algorithm was a little (or a lot) off and that they still had a long way to go. I [...]