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pro vyhledávání: '"Wang, Daixin"'
Autor:
Wang, Yakun, Wang, Daixin, Liu, Hongrui, Hu, Binbin, Yan, Yingcui, Zhang, Qiyang, Zhang, Zhiqiang
Link prediction, as a fundamental task for graph neural networks (GNNs), has boasted significant progress in varied domains. Its success is typically influenced by the expressive power of node representation, but recent developments reveal the inferi
Externí odkaz:
http://arxiv.org/abs/2407.20499
Uplift modeling aims to measure the incremental effect, which we call uplift, of a strategy or action on the users from randomized experiments or observational data. Most existing uplift methods only use individual data, which are usually not informa
Externí odkaz:
http://arxiv.org/abs/2403.06489
User financial default prediction plays a critical role in credit risk forecasting and management. It aims at predicting the probability that the user will fail to make the repayments in the future. Previous methods mainly extract a set of user indiv
Externí odkaz:
http://arxiv.org/abs/2403.06482
Autor:
Xie, Beini, Chang, Heng, Zhang, Ziwei, Wang, Xin, Wang, Daixin, Zhang, Zhiqiang, Ying, Rex, Zhu, Wenwu
Graph Neural Networks (GNNs) obtain tremendous success in modeling relational data. Still, they are prone to adversarial attacks, which are massive threats to applying GNNs to risk-sensitive domains. Existing defensive methods neither guarantee perfo
Externí odkaz:
http://arxiv.org/abs/2304.04168
Autor:
Wang, Xin, Chang, Heng, Xie, Beini, Bian, Tian, Zhou, Shiji, Wang, Daixin, Zhang, Zhiqiang, Zhu, Wenwu
Publikováno v:
IEEE Transactions on Knowledge and Data Engineering 2023 (IEEE TKDE 2023)
Graph neural networks (GNNs) have achieved tremendous success in the task of graph classification and its diverse downstream real-world applications. Despite the huge success in learning graph representations, current GNN models have demonstrated the
Externí odkaz:
http://arxiv.org/abs/2208.06651
Knowledge graph is generally incorporated into recommender systems to improve overall performance. Due to the generalization and scale of the knowledge graph, most knowledge relationships are not helpful for a target user-item prediction. To exploit
Externí odkaz:
http://arxiv.org/abs/2111.02100
Publikováno v:
In Chinese Journal of Aeronautics January 2024 37(1):256-270
Autor:
Lin, Jianbin, Wang, Daixin, Guan, Lu, Zhao, Yin, Zhao, Binqiang, Zhou, Jun, Li, Xiaolong, Qi, Yuan
Nowadays designing a real recommendation system has been a critical problem for both academic and industry. However, due to the huge number of users and items, the diversity and dynamic property of the user interest, how to design a scalable recommen
Externí odkaz:
http://arxiv.org/abs/2003.07158
Autor:
Wang, Daixin, Lin, Jianbin, Cui, Peng, Jia, Quanhui, Wang, Zhen, Fang, Yanming, Yu, Quan, Zhou, Jun, Yang, Shuang, Qi, Yuan
With the rapid growth of financial services, fraud detection has been a very important problem to guarantee a healthy environment for both users and providers. Conventional solutions for fraud detection mainly use some rule-based methods or distract
Externí odkaz:
http://arxiv.org/abs/2003.01171
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