I have a graph with following properties:
Graph(num_nodes=100, num_edges=17144,
ndata_schemes={'feat': Scheme(shape=(2,), dtype=torch.float64), 'label': Scheme(shape=(), dtype=torch.int64), 'train_mask': Scheme(shape=(), dtype=torch.bool), 'val_mask': Scheme(shape=(), dtype=torch.bool), 'test_mask': Scheme(shape=(), dtype=torch.bool)}
edata_schemes={'weight': Scheme(shape=(18,), dtype=torch.float64)})
I want to perform edge classification on this graph, so following functions are needed:
class GCN():
def __init__(self):
pass
def forward(self):
pass
def train():
pass
def test():
pass
can anyone please provide some starting point to complete these functions
Will passing in concatenated features [src, edge, dst] in network same as node classification one do the job? though will need some help with the theory if this is the correct way