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CASIA OpenIR
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Inductive Representation Learning on Dynamic Stock Co-Movement Graphs for Stock Predictions 期刊论文
INFORMS JOURNAL ON COMPUTING, 2022, 页码: 19
作者:  Tian, Hu;  Zheng, Xiaolong;  Zhao, Kang;  Liu, Maggie Wenjing;  Zeng, Daniel Dajun
Adobe PDF(1329Kb)  |  收藏  |  浏览/下载：341/77  |  提交时间：2022/07/25
graph representation learning  deep learning  predictive models  business intelligence  
epsilon-k anonymization and adversarial training of graph neural networks for privacy preservation in social networks 期刊论文
ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2021, 卷号: 50, 页码: 17
作者:  Tian, Hu;  Zheng, Xiaolong;  Zhang, Xingwei;  Zeng, Daniel Dajun
Adobe PDF(2484Kb)  |  收藏  |  浏览/下载：300/102  |  提交时间：2021/12/28
Privacy preservation  Anonymization  Graph neural networks  Social network  
HackRL: Reinforcement learning with hierarchical attention for cross-graph knowledge fusion and collaborative reasoning 期刊论文
KNOWLEDGE-BASED SYSTEMS, 2021, 卷号: 233, 页码: 14
作者:  Yang, Linyao;  Wang, Xiao;  Dai, Yuxin;  Xin, Kejun;  Zheng, Xiaolong;  Ding, Weiping;  Zhang, Jun;  Wang, Fei-Yue
Adobe PDF(1159Kb)  |  收藏  |  浏览/下载：398/137  |  提交时间：2021/12/28
Knowledge fusion  Knowledge reasoning  Decision-making  Hierarchical graph attention  Reinforcement learning  
Intelligent-Commerce Research in China 期刊论文
IEEE INTELLIGENT SYSTEMS, 2008, 卷号: 23, 期号: 6, 页码: 14-18
作者:  Zeng, Daniel;  Wang, Fei-Yue;  Zheng, Xiaolong;  Yuan, Yong;  Chen, Guoqing;  Chen, Jian
Adobe PDF(239Kb)  |  收藏  |  浏览/下载：341/77  |  提交时间：2015/11/08
Intelligent-Commerce Research in China 期刊论文
IEEE INTELLIGENT SYSTEMS, 2008, 卷号: 23, 期号: 6, 页码: 14-18
作者:  Zeng, Daniel;  Wang, Fei-Yue;  Zheng, Xiaolong;  Yuan, Yong;  Chen, Guoqing;  Chen, Jian
浏览  |  Adobe PDF(239Kb)  |  收藏  |  浏览/下载：296/67  |  提交时间：2015/11/08
Intelligent-commerce  
Social balance in signed networks 期刊论文
INFORMATION SYSTEMS FRONTIERS, 2015, 卷号: 17, 期号: 5, 页码: 1077-1095
作者:  Zheng, Xiaolong;  Zeng, Daniel;  Wang, Fei-Yue
浏览  |  Adobe PDF(761Kb)  |  收藏  |  浏览/下载：427/124  |  提交时间：2015/11/04
Social Balance  Signed Networks  Empirical Study  Dynamicsmodel