Day 25 : Graphs

So I scaled the graphs for upto 50,000 agents, and they look like

for BA model, and like

for the modified model.

A sample graph, for 30 agents, look like this

Trying out some neighboring effects on this graph, we see the following results.

The neighbours of "Agent 5" show up in blue.

And then neighbours of "Agent 1" show up in pink.

It's interesting to see the transition of "Agent 5" itself. 

However, still a lot of work needs to be done to map out the collective effects on the non-hub nodes. So Long.

Day 24 : Modifiers!

So today, I tested out the 'Like Mindedness' attribute as a modifier for the connectivity probability on top of the basic BA model.
The result for 1000 and 10,000 agents respectively are as follows :

1. 1000 agents, with average connectivity of 4, under the basic BA model

2. same model with modifier

We can see the decrease in the highest peak value.

3. 10,000 agents, with average connectivity of 4, under the basic BA model

4. same model with modifier

The change in behaviour seems consistent even with the increase in the number of agents.

Next up, I scale it for even larger models, and try out composite modifiers. So Long. 

Day 23 : Progress!

Finally, having rested up and consumed loads of Indian food in Hungary, I am back!
This week, I started with a fresh approach to create a BA network, based on an edge-list view instead of going for separate classes, and the output looks like this for 1000 nodes, when seeded with a fully-connected network of 5 agents.

  The same network, scaled for a 10,000 agents, now takes less than a minute and looks like this

In both cases, the average degree goes to 4. 

Now, I'll link this model to the existing class structure and see how using the belief values or 'likemindedness' affects the connections. 

So Long.

Day 22 : Graphs!

I have been gone for a while. This duration, I made a lot of changes, and now the result I have currently looks like the following.

Few days ago, it started getting in shape, a basic, 10-node network based on 3-node seed network looked like this.

Then, increasing the number of nodes to 500 resulted in this.

Clearly, it's lacking in the number of connections being formed, as opposed to normal PA models tht do not have any connection forming criteria. However, I tried to increase the number of connections, and got something like this for the same number of nodes

The sudden shift and spike in number of connections is clear, but the second peak is against the nature, so I reverted back, and tried a different approach. While the above model used random number of connections for the incoming nodes, I tried with (seed/2) number of connections instead. And it yielded in the following result for 150 nodes,

And for 700 nodes, it looked like

Now, again, I will be away for the weekend, but the new tests should yield results when I resume with the simulations next week.

So Long.

Day 21 : Video!

So, since I still have a hard time trying to explain to people what is it that I am exactly trying to achieve,  (World Domination! Isn't that obvious??!! ) I stumbled across this amazing video by Hannah Fry , explaining all sorts of things like hub centres, preferential attachment, and even degrees of connections to some extent.
Go have fun!

Day 20 : New Approach.

So, Mathematica seems easy to handle, and this change comes with a new approach.

Step 1. Create a Network with fixed number of nodes and connections.
Step 2. Decide on a probability value.
Step 3. Based on that probability, randomly rewire the connections.

This generates something like this for 500 agents, and 1000 connections total.

Advantages :
1. Faster Execution
2. Lesser LOC.

Disadvantages :
Working on it.
So Long.