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MultNet: An Efficient Network Representation Learning for Large-Scale Social Relation Extraction

  • Jun Yuan
  • Neng Gao
  • Lei Wang
  • Zeyi Liu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11303)

Abstract

Network representation learning (NRL), which has become an focus of current research, learns low-dimensional vertex representations to capture network information. However, conventional NRL models either largely neglect the rich semantic information on edges and fail to extract good features of relations, or employ complex models that have rather high space and time complexities. In this work, we present an efficient NRL model, MultNet, for Social Relation Extraction (SRE) task, which evaluates the ability of NRL models on modeling the relationships between vertices. We conduct extensive experiments on several public data sets and experiments on SRE indicate that MultNet outperforms other baseline models significantly.

Keywords

Network representation learning Embedding Social Relation Extraction 

Notes

Acknowledgement

This work is supported by the National Key Research and Development Program of China (No. 2016YFB0800504), and National Natural Science Foundation of China (No. U163620068).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.School of Cyber SecurityUniversity of Chinese Academy of SciencesBeijingChina
  2. 2.Data Assurance and Communications Security CenterChinese Academy of SciencesBeijingChina
  3. 3.Institute of Information EngineeringChinese Academy of SciencesBeijingChina

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