In the NegativeSampler class,
class NegativeSampler(object):
def __init__(self, g, k, neg_share=False):
self.weights = g.in_degrees().float() ** 0.75
self.k = k
self.neg_share = neg_share
def __call__(self, g, eids):
src, _ = g.find_edges(eids)
n = len(src)
if self.neg_share and n % self.k == 0:
dst = self.weights.multinomial(n, replacement=True)
dst = dst.view(-1, 1, self.k).expand(-1, self.k, -1).flatten()
else:
dst = self.weights.multinomial(n*self.k, replacement=True)
src = src.repeat_interleave(self.k)
return src, dst
Can someone help me understand what the __call__
method doing? If my understanding ins correct, self.weights.multinomial()
only returns the dst nodes of the edges with a multinomial distribution. How does it gets negative dst nodes in here?
Thank you!