Given a very large graph, containing a small number of tagged vertices, we want to be able to label a given node/nodes, with pre-training only on sub-graphs with limited size only.

To run the code, run the file subgraphs_GCN_reg.py. This file can run the following:

1. small datasets (Cora,CiteSeer, Pubmed, CS).
2. Large datasets from GraphSaint git.

In order to run the large datasets, change in main Graph_Saint option to be True. Otherwise, you will run the small datasets, there you can choose one of the following inputs:

1. BOW (change BOW = True in main)
2. 2k vectors input (Bow=False). This feature represents the number of first and second neighbors belonging to each class in the training set. For example, assume a classification task with 3 possible labels, and a node with 5 neighbors and 30 second neighbors. Further assume that 1 first neighbor belongs to the training set and has label A, 3 second neighbors belong to the training set and have label A and 1 second neighbor belongs to the training set and has label B. The input to the node would be [1,0,0,3,1,0], where the first three values represent the first neighbors and the last three values represent the second neighbors. Code for creating this feature can be found in pre-process.py file.