Predictive network representation learning for link prediction

Zhitao Wang, Chengyao Chen, Wenjie Li

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

20 Citations (Scopus)

Abstract

In this paper, we propose a predictive network representation learning (PNRL) model to solve the structural link prediction problem. The proposed model de-nes two learning objectives, i.e., observed structure preservation and hidden link prediction. To integrate the two objectives in a unified model, we develop an e-ective sampling strategy to select certain edges in a given network as assumed hidden links and regard the rest network structure as observed when training the model. By jointly optimizing the two objectives, the model can not only enhance the predictive ability of node representations but also learn additional link prediction knowledge in the representation space. Experiments on four real-world datasets demonstrate the superiority of the proposed model over the other popular and state-of-The-Art approaches.
Original languageEnglish
Title of host publicationSIGIR 2017 - Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval
PublisherAssociation for Computing Machinery, Inc
Pages969-972
Number of pages4
ISBN (Electronic)9781450350228
DOIs
Publication statusPublished - 7 Aug 2017
Event40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017 - Tokyo, Shinjuku, Japan
Duration: 7 Aug 201711 Aug 2017

Conference

Conference40th International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR 2017
CountryJapan
CityTokyo, Shinjuku
Period7/08/1711/08/17

Keywords

  • Network Representation Learning; Link Prediction

ASJC Scopus subject areas

  • Information Systems
  • Software
  • Computer Graphics and Computer-Aided Design

Cite this