Graphsage example probability in sampling

Models & Apps
,
#1

Hi,

I am looking at the Graphsage example and in particular the sampling portion from line 197 of train_sampling_unsupervised.py

sampler = dgl.dataloading.MultiLayerNeighborSampler(
        [int(fanout) for fanout in args.fan_out.split(',')])
    dataloader = dgl.dataloading.EdgeDataLoader(
        g, train_seeds, sampler, exclude='reverse_id',
        # For each edge with ID e in Reddit dataset, the reverse edge is e ± |E|/2.
        reverse_eids=th.cat([
            th.arange(n_edges // 2, n_edges),
            th.arange(0, n_edges // 2)]),
        negative_sampler=NegativeSampler(g, args.num_negs),
        batch_size=args.batch_size,
        shuffle=True,
        drop_last=False,
        pin_memory=True,
        num_workers=args.num_workers)

Is there a way to provide edge weights as probability to this sampling mechansim? I noticed that the dgl.sampling functions are able to take as input a probability vector. However this does not seem to be the case for MultiLayerNeighborSampler.

Any help would be appreciated.

Thanks,
Faraz

#2

For now MultiLayerNeighborSampler does not have edge weights supported. You will need to modify it by changing the sample_neighbors call inside by adding a probability vector argument.