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Combining Node Identifier Features and Community Priors for Within-Network Classification

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Book cover Web and Big Data (APWeb-WAIM 2017)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10367))

Abstract

With widely available large-scale network data, one hot topic is how to adopt traditional classification algorithms to predict the most probable labels of nodes in a partially labeled network. In this paper, we propose a new algorithm called identifier based relational neighbor classifier (IDRN) to solve the within-network multi-label classification problem. We use the node identifiers in the egocentric networks as features and propose a within-network classification model by incorporating community structure information to predict the most probable classes for unlabeled nodes. We demonstrate the effectiveness of our approach on several publicly available datasets. On average, our approach can provide Hamming score, Micro-\(\text {F}_1\) score and Macro-\(\text {F}_1\) score up to 14%, 21% and 14% higher than competing methods respectively in sparsely labeled networks. The experiment results show that our approach is quite efficient and suitable for large-scale real-world classification tasks.

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Notes

  1. 1.

    https://github.com/yeqi-adrs/IDRN.

  2. 2.

    http://www.imdb.com/interfaces.

  3. 3.

    http://leitang.net/social_dimension.html.

  4. 4.

    https://github.com/phanein/deepwalk.

  5. 5.

    https://github.com/tangjianpku/LINE.

  6. 6.

    https://github.com/sharadnandanwar/snbc.

  7. 7.

    https://github.com/aditya-grover/node2vec.

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Acknowledgments

The authors would like to thank all the members in ADRS (ADvertisement Research for Sponsered search) group in Sogou Inc. for the help with parts of the data processing and experiments.

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Authors and Affiliations

  1. Sogou Inc., Beijing, China

    Qi Ye, Changlei Zhu, Gang Li & Feng Wang

Authors
  1. Qi Ye
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  2. Changlei Zhu
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  3. Gang Li
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  4. Feng Wang
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Corresponding author

Correspondence to Qi Ye .

Editor information

Editors and Affiliations

  1. Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China

    Lei Chen

  2. Computer Science, Aarhus University, Aarhus N, Denmark

    Christian S. Jensen

  3. Computer Science, University of Southern California, Los Angeles, California, USA

    Cyrus Shahabi

  4. Northeastern University , Shenyang, China

    Xiaochun Yang

  5. Kent State University, Kent, Ohio, USA

    Xiang Lian

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Ye, Q., Zhu, C., Li, G., Wang, F. (2017). Combining Node Identifier Features and Community Priors for Within-Network Classification. In: Chen, L., Jensen, C., Shahabi, C., Yang, X., Lian, X. (eds) Web and Big Data. APWeb-WAIM 2017. Lecture Notes in Computer Science(), vol 10367. Springer, Cham. https://doi.org/10.1007/978-3-319-63564-4_1

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  • DOI: https://doi.org/10.1007/978-3-319-63564-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-63563-7

  • Online ISBN: 978-3-319-63564-4

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