gensim word2vec results - why non-nearby word first?

from gensim.models import Word2Vec
model = Word2Vec(sentences = [['a','b'],['c','d']], window = 9999999, min_count=1)
model.wv.most_similar('a', topn=10) 

Above code gives the following result:

[('d', 0.06363436579704285),
 ('b', -0.010543467476963997),
 ('c', -0.039232250303030014)]

shouldn't the 'b' ranked first, since it's the only one nearby 'a'?