Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks-Reference-Cited by-同舟云学术

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Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks

Published:2024-06 Issue: Volume:294 Page:111714
ISSN:0950-7051
Container-title:Knowledge-Based Systems
language:en
Short-container-title:Knowledge-Based Systems

Author:

Wu Yajing^ORCID,Zhang Chenyang,Tang Yongqiang^ORCID,Yang Xuebing^ORCID,Yin Yanting,Zhang Wensheng

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference48 articles.

1. Dynamic network embedding survey;Xue;Neurocomputing,2022

2. E. Hajiramezanali, A. Hasanzadeh, K. Narayanan, N. Duffield, M. Zhou, X. Qian, Variational Graph Recurrent Neural Networks, in: Proc. 33rd Int. Conf. Neural Inf. Process. Syst., 2019, pp. 10700–10710.

3. Scalable temporal latent space inference for link prediction in dynamic social networks;Zhu;IEEE Trans. Knowl. Data Eng.,2016

4. User behavior prediction model based on implicit links and multi-type rumor messages;Knowl.-Based Syst.,2023

5. DynGEM: Deep embedding method for dynamic graphs;Goyal,2018

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