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A Label Inference Method Based on Maximal Entropy Random Walk over Graphs

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Web Technologies and Applications (APWeb 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9931))

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Abstract

With the rapid development of Internet, graphs have been widely used to model the complex relationships among various entities in real world. However, the labels on the graphs are always incomplete. The accurate label inference is required for many real applications such as personalized service and product recommendation. In this paper, we propose a novel label inference method based on maximal entropy random walk. The main idea is that a small number of vertices in graphs propagate their labels to other unlabeled vertices in a way of random walk with the maximal entropy guidance. We give the algorithm and analyze the time and space complexities. We confirm the effectiveness of our algorithm through conducting experiments on real datasets.

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Acknowledgments

This work is supported by the grant of the National Natural Science Foundation of China No. 61432011, 61402323 and 61502335.

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Authors and Affiliations

  1. School of Computer Science and Technology, Tianjin University, Tianjin, China

    Jing Pan, Yajun Yang, Qinghua Hu & Hong Shi

  2. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China

    Jing Pan, Yajun Yang, Qinghua Hu & Hong Shi

Authors
  1. Jing Pan
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  2. Yajun Yang
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  3. Qinghua Hu
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  4. Hong Shi
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Corresponding author

Correspondence to Yajun Yang .

Editor information

Editors and Affiliations

  1. School of Computing, University of Utah, Salt Lake City, Utah, USA

    Feifei Li

  2. School of Electrical Engineering, Seoul National University, Seoul, Korea (Republic of)

    Kyuseok Shim

  3. Soochow University , Suzhou, China

    Kai Zheng

  4. Soochow University , Suzhou, China

    Guanfeng Liu

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Pan, J., Yang, Y., Hu, Q., Shi, H. (2016). A Label Inference Method Based on Maximal Entropy Random Walk over Graphs. In: Li, F., Shim, K., Zheng, K., Liu, G. (eds) Web Technologies and Applications. APWeb 2016. Lecture Notes in Computer Science(), vol 9931. Springer, Cham. https://doi.org/10.1007/978-3-319-45814-4_41

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  • DOI: https://doi.org/10.1007/978-3-319-45814-4_41

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-45813-7

  • Online ISBN: 978-3-319-45814-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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