def jaccard_similarity(A, B):
intersection = np.count_nonzero(A * B, axis=1)
J = intersection * 1.0 / (np.count_nonzero(A, axis=1) + np.count_nonzero(
B, axis=1) + intersection + gg.epsilon())
return J
def cosine_similarity(A, B):
inner_product = (A * B).sum(1)
C = inner_product / (np.sqrt(np.square(A).sum(1)) *
np.sqrt(np.square(B).sum(1)) + gg.epsilon())
return C
def neighborhood_entropy(node_info, neighbor_infos):
infos = (neighbor_infos.sum(0) + node_info) / (neighbor_infos.shape[0] + 1)
entropy = infos * np.log2(infos + gg.epsilon())
return -np.sum(entropy)
