Machine Learning/Kaggle Social Network Contest/Problem Representation
- come up with a plan of attack.
Idea C - two hop neighbourhood
For each node of 38k sampled individuals calculate features based on the a two hop neighbourhood -ie friends of friends.
- these neighbourhoods should make the problem a little more tractable.
Idea A - huge CSV
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.
Suggestion on how to tame the size: Perhaps breaking into two csvs with one for data columns about nodes and another for columns about edges. For example:
- node_features.csv: node_id, inbound_degree, outbound_degree, clustering coefficient, etc, etc.
- edge_features.csv: node_i, node_j, shortest_distance, density, etc, etc
If both types of data are in one datafile, we'd be probably be duplicating any single-node-centric data points for every single edge row. I understand we might need to ultimately need to create such a single file, but I feel like two files will help keep it manageable as we identify and calculate feature data in the short term.
UPDATE: Network Size is definitely going to be an issue. Traversing the network to calculate shortest_distance crashed and burned with memory shortfalls. Initially I thought it was manageable until I allowed my code to use the entire adjacency list. Even without outputting data for edges with no path between them, I think the data dump storage itself would also be a problem. I am going to try rerunning the shortest_distance search with a max depth limit of
six three to see if that makes it more manageable.
UPDATE 2: The BFS search approach I was taking to path finding was too slow since I kept retracing the data, so I just tried scripting a dump of all "friend-of-friends" edges. After 5.5 hours computation time I have "friend-of-friend" edge dump but it's a 6.5GB file. That said, I accidentally included duplicate edges, so hopefully after deduping it will be um smaller.
Idea B - online learning
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.
Can whoever added/proposed this please flesh this out more? Curious to explore alternative approaches for sure, even if they seem computationally difficult.