  We propose a similarity-based method, using the similarity between nodes, to
address the problem of classification in partially labeled networks. The basic
assumption is that two nodes are more likely to be categorized into the same
class if they are more similar. In this paper, we introduce ten similarity
indices, including five local ones and five global ones. Empirical results on
the co-purchase network of political books show that the similarity-based
method can give high accurate classification even when the labeled nodes are
sparse which is one of the difficulties in classification. Furthermore, we find
that when the target network has many labeled nodes, the local indices can
perform as good as those global indices do, while when the data is sparce the
global indices perform better. Besides, the similarity-based method can to some
extent overcome the unconsistency problem which is another difficulty in
classification.
