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Line graph contrastive learning for link prediction

  • Published:2023-08 Issue: Volume:140 Page:109537
  • ISSN:0031-3203
  • Container-title:Pattern Recognition
  • language:en
  • Short-container-title:Pattern Recognition

Author:

Zhang ZehuaORCID,Sun Shilin,Ma Guixiang,Zhong Caiming

Funder

Shanxi Scholarship Council of China

National Natural Science Foundation of China

Ministry of Education of the People's Republic of China

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference43 articles.

1. Integrating node centralities, similarity measures, and machine learning classifiers for link prediction;Anand;Multimed. Tools Appl.,2022

2. Predicting biomedical interactions with higher-order graph convolutional networks;Kishan;IEEE/ACM Trans. Comput. Biol.Bioinf.,2021

3. Learning graph structure via graph convolutional networks;Zhang;Pattern Recognit.,2019

4. Link prediction techniques, applications, and performance: a survey;Kumar;Physica A Stat. Mech.Appl.,2020

5. Neural graph embeddings as explicit low-rank matrix factorization for link prediction;Agibetov;Pattern Recognit.,2023

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