Tumbling, Appendix I: Making Connections

Where Things Get Interesting

Recall in Part 3 (under 'Notes and Improvements'), I mentioned the possibility of creating graphs of how a post spreads - ie who reblogged who.

As it turns out it was possible. And in fact, not that difficult.

You can find the modified code here.

In addition to the outputs the previous iteration generated, this updated version also creates a 'graph.csv' file, which contains all the links between reblogging users. This file can then be imported into, for example, Gephi, to create a visualisation of the graph.


Modified Model

The updated model works thing like this

We have the set, F, of people who reblogged in the previous generation. For each of these users we iterate over their followers, with the reaction code as before.

But we now have the addition that, if the follower reblogs, a new property - user.reblogged - is set to the person they reblogged from. That (reblogging) user is then added to the temporary group f_i to be used in the next iteration.

The simulation ends when f_i is empty - ie, no-one reblogs.


Pretty as a Picture

So, keeping the initial conditions the same as for Part 3, here is an example output

And here if the accompanying 'spread over eccentricity' graph.

Compare for those visualisations in Part 1

I have to say, I am extremely pleased with this. I mean, just look at it! It's amazing.

Here's another example

And finally, here's a graph for a population size, N = 20,000 & f0 = 1,000

More reblogs overall, and lots more clustering in this case. Pretty awesome, right?


Little Niggles

The only things that really bother me now:

1) The Like count seems a little high
This is probably a result of the distribution/variables chosen in creating approval thresholds.

2) There's only a small number of reblogs coming from OP.
This relates to the 'reblogs from tags' thing discussed in Part 3.

3) Small population
At some point, when I've got the time to run the simulation, I'd like to try it with larger populations/larger f0

But none of these things bother me too much, and I'm pleased with what I've got.


So that's basically that.


Oatzy.