Machine Learning/Kaggle Social Network Contest/Problem Representation
- someone with large memory (>5.5GB) double check the number of unique nodes by loading it in networkx
- come up with a plan of attack.
Construct a huge csv file containing each possible directed link and a bunch of features associated with it, then do some supervised learning on it.
It would have the following format
node_i, node_j, feature_ij_1, feature_ij_2, ...
The length of this would be long. When loading 3M rows of the edge list file I get 732166 nodes which means that this file would need (732 166^2) - 732 166 = 536 066 319 390 rows.
Say each column took up took up 7 characters and there were 12 columns (ie 10 features) we'd have a row of size 84 bytes. This makes it about 4.5 x10^13 bytes = 41 937 gigabytes
This is just if we use the first 3 million rows.
(Note if I have miscounted the number of unique nodes and there really are only 38k we'd still be dealing with a 112 GB file.)
This number could be culled by considering just the nodes in some neighbourhood - but I figure that would only provide us with information about nodes which are connected.
We could perform some kind of online learning on the network where compute features based on a pair of nodes and then update of parameters. This would take 500 billion steps - which sounds like a lot (again just based on the first 3M rows from the edge file).