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Weighted Clustering Coefficients Based Feature Extraction and Selection for Collaboration Relation Prediction

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Data Science (ICPCSEE 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 901))

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Abstract

Existing methods of scientific collaboration prediction often use one-step weighted attribute of the common neighbor to construct associated feature. Such methods make no distinction between the different contributions of different neighbors and they cannot effectively deal with the redundant information between the features. Consequently, a general feature-based framework for weighted scientific network relation prediction is proposed. The framework is based on the heuristics of the Naive Bayes link prediction model. Firstly, we introduce weighted clustering coefficients to define several weighted Naive Bayes features, then use the mRMR feature selection method to deal with the information between the SE relevant features. Extensive experiments on real-world scientific network datasets demonstrate that the effectiveness of our proposed framework. Further experiments are conducted to understand the impacts of leveraging of extraction and selection of the proposed weighted features.

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Notes

  1. 1.

    http://www.linkprediction.org/index.php/link/resource/data.

  2. 2.

    http://www-personal.umich.edu/~mejn/netdata/.

  3. 3.

    http://opsahl.co.uk/tnet/datasets/.

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Acknowledgements

Jiehua Wu is supported by Guangdong Provincial Higher outstanding young teachers Training Program (Nos. YQ2015177). This work is supported by Natural Science Foundation of Guangdong Province (No. 2017ZC0348). Guangdong Provincial major scientific research project (No. 2017GKTSCX009) and Major engineering technical and commercial services applied research project (No. GDGM2015-ZZ-C03).

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Authors and Affiliations

  1. School of Computer and Information Engineering, Guangdong Polytechnic of Industry and Commerce, Guangzhou, 510510, China

    Jiehua Wu

  2. School of Computer Science and Engineering, South China University of Technology, Guangzhou, 510641, China

    Jiehua Wu

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  1. Jiehua Wu
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Correspondence to Jiehua Wu .

Editor information

Editors and Affiliations

  1. Zhengzhou University, Zhengzhou, Henan, China

    Qinglei Zhou

  2. Zhengzhou University of Light Industry, Zhengzhou, Henan, China

    Yong Gan

  3. Northeast Forestry University, Harbin, China

    Weipeng Jing

  4. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  5. Zhengzhou Institute of Technology, Zhengzhou, China

    Yan Wang

  6. National Academy of Guo Ding Institute of Data Science, Beijing, China

    Zeguang Lu

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Wu, J. (2018). Weighted Clustering Coefficients Based Feature Extraction and Selection for Collaboration Relation Prediction. In: Zhou, Q., Gan, Y., Jing, W., Song, X., Wang, Y., Lu, Z. (eds) Data Science. ICPCSEE 2018. Communications in Computer and Information Science, vol 901. Springer, Singapore. https://doi.org/10.1007/978-981-13-2203-7_12

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  • DOI: https://doi.org/10.1007/978-981-13-2203-7_12

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  • Online ISBN: 978-981-13-2203-7

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