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Daixin Wang
Researcher at Tsinghua University
Publications - 19
Citations - 2951
Daixin Wang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 7, co-authored 13 publications receiving 2175 citations.
Papers
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Proceedings ArticleDOI
Structural Deep Network Embedding
Daixin Wang,Peng Cui,Wenwu Zhu +2 more
TL;DR: This paper proposes a Structural Deep Network Embedding method, namely SDNE, which first proposes a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non- linear network structure and exploits the first-order and second-order proximity jointly to preserve the network structure.
Proceedings ArticleDOI
A Semi-Supervised Graph Attentive Network for Financial Fraud Detection
Daixin Wang,Yuan Qi,Jianbin Lin,Peng Cui,Quanhui Jia,Wang Zhen,Yanming Fang,Quan Yu,Jun Zhou,Shuang Yang +9 more
TL;DR: Li et al. as mentioned in this paper proposed a semi-supervised attentive graph neural network (SemGNN) to utilize the multi-view labeled and unlabeled data for fraud detection.
Proceedings ArticleDOI
A Semi-supervised Graph Attentive Network for Financial Fraud Detection
Daixin Wang,Jianbin Lin,Peng Cui,Quanhui Jia,Wang Zhen,Yanming Fang,Quan Yu,Jun Zhou,Shuang Yang,Yuan Qi +9 more
TL;DR: Li et al. as discussed by the authors proposed a semi-supervised attentive graph neural network (SemiGNN) to utilize the multi-view labeled and unlabeled data for fraud detection.
Proceedings ArticleDOI
Deep Variational Network Embedding in Wasserstein Space
TL;DR: The experimental results demonstrate that the proposed Deep Variational Network Embedding in Wasserstein Space (DVNE) can effectively model the uncertainty of nodes in networks, and show a substantial gain on real-world applications such as link prediction and multi-label classification compared with the state-of-the-art methods.
Proceedings Article
Deep multimodal hashing with orthogonal regularization
TL;DR: This paper proposes a novel deep multimodal hashing method, namely Deep Multimodal Hashing with Orthogonal Regularization (DMHOR), which fully exploits intra- modality and inter-modality correlations and finds that a better representation can be attained with different numbers of layers for different modalities, due to their different complexities.