negative_sampler.py

import torch as th
import dgl

class NegativeSampler(object):
    def __init__(self, g, k, neg_share=False, device=None):
        if device is None:
            device = g.device
        self.weights = g.in_degrees().float().to(device) ** 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