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Link Mining in Online Social Networks with Directed Negative Relationships

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Social Computing (ICYCSEE 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 623))

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Abstract

One of the most important work to analyse online social networks is link mining. A new type of social networks with positive and negative relationships are burgeoning. We present a link mining method based on random walk theory to mine the unknown relationships in directed social networks which have negative relationships. Firstly, we define an extended Laplacian matrix based on this type of social networks. Then, we prove the matrix can be used to compute the similarities of the node pairs. Finally, we propose a link mining method based on collaboration recommendation method. We apply our method in two real social networks. Experimental results show that our method do better in terms of sign accuracy and AUC for mining unknown links in the two real datasets.

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Acknowledgement

This work is supported by National Natural Science Foundation under Grant (No. 61373149, 61472233, 61572300), Technology Program of Shandong Province under Grant (No.2014GGB01617, ZR2014FM001), Taishan Scholar Program of Shandong Procince(No.TSHW201502038), Exquisite course project of Shandong Province (No. 2012BK294, 2013BK399, and 2013BK402), and Education scientific planning project of Shandong province (No. ZK1437B010).

Author information

Authors and Affiliations

  1. School of Information Technology, Shandong Women’s University, Jinan, 250014, China

    Baofang Hu

  2. School of Information Science and Engineering, Shandong Normal University, Jinan, 250014, China

    Baofang Hu & Hong Wang

Authors
  1. Baofang Hu
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  2. Hong Wang
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Corresponding author

Correspondence to Baofang Hu .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology , Harbin, China

    Wanxiang Che

  2. Harbin Engineering University , Harbin, China

    Qilong Han

  3. Harbin Institute of Technology , Harbin, China

    Hongzhi Wang

  4. Northeast Forestry University , Harbin, China

    Weipeng Jing

  5. National University of Defense Technology , Changsha, China

    Shaoliang Peng

  6. Harbin Engineering University , Harbin, China

    Junyu Lin

  7. Harbin Univ. of Science and Technology , Harbin, China

    Guanglu Sun

  8. Harbin Univ. of Science and Technology , Harbin, China

    Xianhua Song

  9. Harbin Engineering University , Harbin, China

    Hongtao Song

  10. Harbin Sea of Clouds & Computer Tech. , Harbin, China

    Zeguang Lu

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© 2016 Springer Science+Business Media Singapore

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Hu, B., Wang, H. (2016). Link Mining in Online Social Networks with Directed Negative Relationships. In: Che, W., et al. Social Computing. ICYCSEE 2016. Communications in Computer and Information Science, vol 623. Springer, Singapore. https://doi.org/10.1007/978-981-10-2053-7_38

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  • DOI: https://doi.org/10.1007/978-981-10-2053-7_38

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-2052-0

  • Online ISBN: 978-981-10-2053-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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