Fix negative sampling. Avoid invalid samples. Also add regression bias.

[SherylHYX]

The first commit: Fix negative sampling. Avoid sampling positive edges and viewing them as non-edges, and avoid sampling negative edges and viewing them as non-edges. Borrow utility functions from torch_geometric.
The trick to avoiding sampling an edge that exists is to map the existent links and the sampled links to some unique numbers (by multiplying the source node with the total number of nodes, then add the target node, ref: https://github.com/rusty1s/pytorch_geometric/blob/712f581642bccc225189fc3eb364ce0642b915a3/torch_geometric/utils/negative_sampling.py#L95) After sampling and mapping, we use NumPy.isin to pick out the invalid links (assumed to be non-existent, but exists), then resample iteratively until no invalid samples exist.

The second commit: Add a bias term in regression to predict link signs. This is to learn some prior distribution of different types of links: positive, negative and none. The bias term can be helpful in dealing with unbalanced links. In the data sets used in SGCN, most links do not exist, and positive links are much more frequent than negative ones.