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Similarity-Based Classification In Partially Labeled Networks

Author

Listed:
  • 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)

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.

Suggested Citation

  • Qian-Ming Zhang & Ming-Sheng Shang & Linyuan Lü, 2010. "Similarity-Based Classification In Partially Labeled Networks," International Journal of Modern Physics C (IJMPC), World Scientific Publishing Co. Pte. Ltd., vol. 21(06), pages 813-824.
    • Handle: RePEc:wsi:ijmpcx:v:21:y:2010:i:06:n:s012918311001549x
    DOI: 10.1142/S012918311001549X
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    Cited by:

    1. Le Li & Junyi Xu & Weidong Xiao & Bin Ge, 2016. "Behavior Based Social Dimensions Extraction for Multi-Label Classification," PLOS ONE, Public Library of Science, vol. 11(4), pages 1-16, April.
    2. Zhou, Yinzuo & Wu, Chencheng & Tan, Lulu, 2021. "Biased random walk with restart for link prediction with graph embedding method," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 570(C).
    3. Haji Gul & Feras Al-Obeidat & Adnan Amin & Fernando Moreira & Kaizhu Huang, 2022. "Hill Climbing-Based Efficient Model for Link Prediction in Undirected Graphs," Mathematics, MDPI, vol. 10(22), pages 1-15, November.

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