Zhang, Qian-Ming Shang, Ming-Sheng Lü, Linyuan 2010 Two main difficulties in the problem of classification in partially labeled networks are the sparsity of the known labeled nodes and inconsistency of label information. To address these two difficulties, we propose a similarity-based method, where 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 defined based on the network structure. Empirical results on the co-purchase network of political books show that the similarity-based method can, to some extent, overcome these two difficulties and give higher accurate classification than the relational neighbors method, especially when the labeled nodes are sparse. Furthermore, we find that when the information of known labeled nodes is sufficient, the indices considering only local information can perform as good as those global indices while having much lower computational complexity. application/pdf https://folia.unifr.ch/global/documents/301750 https://folia.unifr.ch/documents/301750/files/lu_sbc.pdf eng info:eu-repo/semantics/altIdentifier/doi/10.1142/S012918311001549X info:eu-repo/semantics/openAccess License undefined International Journal of Modern Physics C. - 2010, vol. 21, no. 6, p. 813-824 Complex networks similarity index classification labeled networks info:eu-repo/classification/udc/53 Similarity-based classification in partially labeled networks http://purl.org/coar/resource_type/c_6501