no code implementations • 11 Mar 2024 • Dingyuan Zhu, Daixin Wang, Zhiqiang Zhang, Kun Kuang, Yan Zhang, Yulin kang, Jun Zhou
The estimator is general for all types of outcomes, and is able to comprehensively model the treatment and control group data together to approach the uplift.
no code implementations • 11 Mar 2024 • Daixin Wang, Zhiqiang Zhang, Yeyu Zhao, Kai Huang, Yulin kang, Jun Zhou
In this paper, we fill in this gap by proposing a motif-preserving Graph Neural Network with curriculum learning (MotifGNN) to jointly learn the lower-order structures from the original graph and higherorder structures from multi-view motif-based graphs for financial default prediction.
no code implementations • CVPR 2023 • Beini Xie, Heng Chang, Ziwei Zhang, Xin Wang, Daixin Wang, Zhiqiang Zhang, Rex Ying, Wenwu Zhu
To tackle these challenges, we propose a novel Robust Neural Architecture search framework for GNNs (G-RNA).
no code implementations • 13 Aug 2022 • Xin Wang, Heng Chang, Beini Xie, Tian Bian, Shiji Zhou, Daixin Wang, Zhiqiang Zhang, Wenwu Zhu
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications.
no code implementations • 3 Nov 2021 • Ke Tu, Peng Cui, Daixin Wang, Zhiqiang Zhang, Jun Zhou, Yuan Qi, Wenwu Zhu
Knowledge graph is generally incorporated into recommender systems to improve overall performance.
no code implementations • 10 Mar 2020 • Jianbin Lin, Daixin Wang, Lu Guan, Yin Zhao, Binqiang Zhao, Jun Zhou, Xiaolong Li, Yuan Qi
However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommendation system, which is able to efficiently produce effective and diverse recommendation results on billion-scale scenarios, is still a challenging and open problem for existing methods.
1 code implementation • 28 Feb 2020 • Daixin Wang, Jianbin Lin, Peng Cui, Quanhui Jia, Zhen Wang, Yanming Fang, Quan Yu, Jun Zhou, Shuang Yang, Yuan Qi
Additionally, among the network, only very few of the users are labelled, which also poses a great challenge for only utilizing labeled data to achieve a satisfied performance on fraud detection.