Z
Zhenyu Hou
Researcher at Tsinghua University
Publications - 11
Citations - 912
Zhenyu Hou is an academic researcher from Tsinghua University. The author has contributed to research in topics: Deep learning & Computer science. The author has an hindex of 4, co-authored 8 publications receiving 146 citations.
Papers
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Journal ArticleDOI
Self-supervised Learning: Generative or Contrastive.
TL;DR: This survey takes a look into new self-supervised learning methods for representation in computer vision, natural language processing, and graph learning, and comprehensively review the existing empirical methods into three main categories according to their objectives.
Journal ArticleDOI
Self-supervised Learning: Generative or Contrastive
TL;DR: Self-supervised representation learning leverages input data itself as supervision and benefits almost all types of downstream tasks as mentioned in this paper, however, its defects of heavy dependence on manual labels and vulnerability to attacks have driven people to find other paradigms.
Proceedings ArticleDOI
GraphMAE: Self-Supervised Masked Graph Autoencoders
TL;DR: This study identifies and examines the issues that negatively impact the development of GAEs, including their reconstruction objective, training robustness, and error metric, and presents a masked graph autoencoder GraphMAE that mitigates these issues for generative self-supervised graph learning.
Journal ArticleDOI
Understanding WeChat User Preferences and “Wow” Diffusion
Fanjin Zhang,Jie Tang,Xueyi Liu,Zhenyu Hou,Yuxiao Dong,Jing Zhang,Xiao Liu,Ruobing Xie,Zhuang Kai,Xu Zhang,Leyu Lin,Philip S. Yu +11 more
TL;DR: A hierarchical graph representation learning based model ProHENE is presented, which is capable of capturing the structured based social observations discovered above and can significantly improve the prediction performance compared with alternative methods.
Posted Content
CogDL: An Extensive Toolkit for Deep Learning on Graphs
Yukuo Cen,Zhenyu Hou,Yan Wang,Qibin Chen,Yizhen Luo,Xingcheng Yao,Aohan Zeng,Shiguang Guo,Peng Zhang,Guohao Dai,Yu Wang,Chang Zhou,Hongxia Yang,Jie Tang +13 more
TL;DR: CogDL as mentioned in this paper is a toolkit for deep learning on graphs that allows researchers and developers to easily conduct experiments and build applications, including node classification, link prediction, graph classification, and other graph tasks.