I am looking at the Graphsage example and in particular the sampling portion from line 197 of
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
Any help would be appreciated.