SIMILARITY-BASED CLASSIFICATION IN PARTIALLY LABELED NETWORKS

Qian-Ming Zhang (), Ming-Sheng Shang () and Linyuan Lü ()
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Qian-Ming Zhang: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
Ming-Sheng Shang: Web Sciences Center, School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 610054, P. R. China
Linyuan Lü: Department of Physics, University of Fribourg, Chemin du Musée 3, Fribourg CH-1700, Switzerland

International Journal of Modern Physics C (IJMPC), 2010, vol. 21, issue 06, 813-824

Abstract: 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.

Keywords: Complex networks; similarity index; classification; labeled networks; 89.20.Ff; 89.75.Hc; 89.65.-s (search for similar items in EconPapers)
Date: 2010
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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DOI: 10.1142/S012918311001549X

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