Machine Learning/Kaggle Social Network Contest/Problem Representation
- 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 node_i's would come from the set of sampled users (ie the 38k outbound nodes).
- The node_j's would come from the union of outbound and inbound nodes (1,133,518 of them)
The length of this would be huge. The file would need about (37689 * 1133547) - 1133547 = 42 721 119 336 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 3,342 gigabytes
If we just consider the 38k outbound nodes 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 42 billion steps - which sounds like a lot.