datasets

Datasets

Our work utilizes a number of graph datasets provided from Stanford SNAP, as well as a Protein-Protein Interaction graph dataset from BioGrid.

We packaged our train/test splits onto: http://sami.haija.org/graph/datasets.tgz, each dataset is in a directory. The directories are:

1. ca-AstroPh
2. ca-HepTh
3. soc-epinions
4. soc-facebook
5. wiki-vote
6. ppi

Each directory contains the files:

1. index.pkl: Python pickle of a dictionary, containing key index that is a mapping from original node ID into integer in range [0, |V| - 1], where |V| are the number of nodes in the graph. This renumbering is important to make sure that node IDs can be represented in an embedding matrix, where the node ID refers to a row in the matrix. All files below uses these integers.
2. train.txt.npy: Positive training edges. Integer numpy array of size (|E|/2, 2), where |E| is the number of edges in the original graph, and therefore |E|/2 is the number of training edges.
3. train.neg.txt.npy: Negative training edges. Integer numpy array of size (|E|/2, 2).
4. test.txt.npy: Positive test edges. Integer numpy array of size (|E|/2, 2).
5. test.neg.txt.npy: Negative test edges. Integer numpy array of size (|E|/2, 2).