2 code implementations • 16 Feb 2023 • Chengbin Hou, Xinyu Lin, Hanhui Huang, Sheng Xu, Junxuan Fan, Yukun Shi, Hairong Lv
Besides, OGS obtains the superior or comparable performance compared to the method under well-known bagging framework.
no code implementations • 20 May 2022 • Bingzhe Wu, Jintang Li, Junchi Yu, Yatao Bian, Hengtong Zhang, Chaochao Chen, Chengbin Hou, Guoji Fu, Liang Chen, Tingyang Xu, Yu Rong, Xiaolin Zheng, Junzhou Huang, Ran He, Baoyuan Wu, Guangyu Sun, Peng Cui, Zibin Zheng, Zhe Liu, Peilin Zhao
Deep graph learning has achieved remarkable progresses in both business and scientific areas ranging from finance and e-commerce, to drug and advanced material discovery.
no code implementations • 15 Feb 2022 • Jintang Li, Bingzhe Wu, Chengbin Hou, Guoji Fu, Yatao Bian, Liang Chen, Junzhou Huang, Zibin Zheng
Despite the progress, applying DGL to real-world applications faces a series of reliability threats including inherent noise, distribution shift, and adversarial attacks.
3 code implementations • 30 May 2021 • Chengbin Hou, Guoji Fu, Peng Yang, Zheng Hu, Shan He, Ke Tang
It is natural to ask if existing DNE methods can perform well for an input dynamic network without smooth changes.
2 code implementations • 5 Aug 2020 • Chengbin Hou, Han Zhang, Shan He, Ke Tang
The main and common objective of Dynamic Network Embedding (DNE) is to efficiently update node embeddings while preserving network topology at each time step.
2 code implementations • arXiv 2019 • Chengbin Hou, Han Zhang, Ke Tang, Shan He
Dynamic network embedding aims to learn low dimensional embeddings for unseen and seen nodes by using any currently available snapshots of a dynamic network.
1 code implementation • 15 Feb 2019 • Guoji Fu, Chengbin Hou, Xin Yao
To tackle this issue, we propose a new NRL framework, named HSRL, to help existing NRL methods capture both the local and global topological information of a network.
Ranked #1 on Link Prediction on Douban
1 code implementation • 28 Nov 2018 • Chengbin Hou, Shan He, Ke Tang
Attributed networks are ubiquitous since a network often comes with auxiliary attribute information e. g. a social network with user profiles.