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Link Prediction on Dynamic Heterogeneous Information Networks

  • Chao KongEmail author
  • Hao Li
  • Liping Zhang
  • Haibei Zhu
  • Tao Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11917)

Abstract

This work develops a broad learning method for link prediction on dynamic heterogeneous information networks. While existing works have primarily focused on dynamic homogeneous networks or static heterogeneous networks. As such, the existing methods can be suboptimal for link prediction on dynamic heterogeneous information networks.

In this paper, we try to study the problem of link prediction combining dynamic networks and heterogeneous networks. However, none of the existing works has paid special attention to connect these two kinds of network data. To tackle this challenge, we propose a new broad learning-based method named HA-LSTM, short for Hierarchical Attention Long-Short Time Memory to address this problem on dynamic heterogeneous information networks. Firstly, we employ the Graph Convolutional Network (GCN) to extract the feature from Heterogeneous Information Networks (HINs). Then, we utilize a broad learning and attention based framework to fuse and extract the information among HINs broadly over timestamps. Finally, the link prediction in time-dimension by employing LSTM could be performed. We conduct extensive experiments on several real dynamic heterogeneous information networks covering the task of link prediction. Both quantitative results and qualitative analysis verify the effectiveness and rationality of our HA-LSTM method.

Keywords

Dynamic heterogeneous information network Link prediction Broad learning 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Chao Kong
    • 1
    Email author
  • Hao Li
    • 1
  • Liping Zhang
    • 1
  • Haibei Zhu
    • 1
  • Tao Liu
    • 1
  1. 1.School of Computer and InformationAnhui Polytechnic UniversityWuhuChina

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