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Constructed Link Prediction Model by Relation Pattern on the Social Network

  • Jimmy Ming-Tai Wu
  • Meng-Hsiun TsaiEmail author
  • Tu-Wei Li
  • Hsien-Chung Huang
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 891)

Abstract

For the link prediction problem, it commonly estimates the similarity by different similarity metrics or machine learning prediction model. However, this paper proposes an algorithm, which is called Relation Pattern Deep Learning Classification (RPDLC) algorithm, based on two neighbor-based similarity metrics and convolution neural network. First, the RPDLC extracts the features for two nodes in a pair, which is calculated with neighbor-based metric and influence nodes. Second, the RPDLC combines the features of nodes to be a heat map for evaluating the similarity of the node’s relation pattern. Third, the RPDLC constructs the prediction model for predicting missing relationship by using convolution neural network architecture. In consequence, the contribution of this paper is purposed a novel approach for link prediction problem, which is used convolution neural network and features by relation pattern to construct a prediction model.

Keywords

Link prediction problem Convolution neural network Relation pattern Social network 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jimmy Ming-Tai Wu
    • 1
  • Meng-Hsiun Tsai
    • 2
    Email author
  • Tu-Wei Li
    • 2
  • Hsien-Chung Huang
    • 3
  1. 1.College of Computer Science and EngineeringShandong University of Science and TechnologyQindaoChina
  2. 2.Department of Management Information SystemsNational Chung Hsing UniversityTaichungTaiwan
  3. 3.Office of Physical Education and SportNational Chung Hsing UniversityTaichungTaiwan

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