16 citations · 18 across the 6 of their papers we have counts for
5 papers · 1 filter
Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework
Chunjing Gan, Binbin Hu, Bo Huang +6
In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspe…
Specformer: Spectral Graph Neural Networks Meet Transformers
Deyu Bo, Chuan Shi, Lele Wang +1
Spectral graph neural networks (GNNs) learn graph representations via spectral-domain graph convolutions. However, most existing spectral graph filters are scalar-to-scalar functio…
Data-Free Adversarial Knowledge Distillation for Graph Neural Networks
Yuanxin Zhuang, Lingjuan Lyu, Chuan Shi +2
Graph neural networks (GNNs) have been widely used in modeling graph structured data, owing to its impressive performance in a wide range of practical applications. Recently, knowl…
KGNN: Distributed Framework for Graph Neural Knowledge Representation
Binbin Hu, Zhiyang Hu, Zhiqiang Zhang +2
Knowledge representation learning has been commonly adopted to incorporate knowledge graph (KG) into various online services. Although existing knowledge representation learning me…
Confidence May Cheat: Self-Training on Graph Neural Networks under Distribution Shift
Hongrui Liu, Binbin Hu, Xiao Wang +3
Graph Convolutional Networks (GCNs) have recently attracted vast interest and achieved state-of-the-art performance on graphs, but its success could typically hinge on careful trai…