79 citations · 234 across the 27 of their papers we have counts for
13 papers · 1 filter
Towards Pre-trained Graph Condensation via Optimal Transport
Yeyu Yan, Shuai Zheng, Wenjun Hui +5
Graph condensation (GC) aims to distill the original graph into a small-scale graph, mitigating redundancy and accelerating GNN training. However, conventional GC approaches heavil…
Dynamic Graph Condensation
Dong Chen, Shuai Zheng, Yeyu Yan +4
Recent research on deep graph learning has shifted from static to dynamic graphs, motivated by the evolving behaviors observed in complex real-world systems. However, the temporal…
FlexCare: Leveraging Cross-Task Synergy for Flexible Multimodal Healthcare Prediction
Muhao Xu, Zhenfeng Zhu, Youru Li +4
Multimodal electronic health record (EHR) data can offer a holistic assessment of a patient's health status, supporting various predictive healthcare tasks. Recently, several studi…
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
Shuai Zheng, Zhizhe Liu, Zhenfeng Zhu +3
Based on the message-passing paradigm, there has been an amount of research proposing diverse and impressive feature propagation mechanisms to improve the performance of GNNs. Howe…
Node-oriented Spectral Filtering for Graph Neural Networks
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +2
Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent…
HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction
Youru Li, Zhenfeng Zhu, Xiaobo Guo +3
Risk prediction, as a typical time series modeling problem, is usually achieved by learning trends in markers or historical behavior from sequence data, and has been widely applied…
Multi-modal Graph Learning for Disease Prediction
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +4
Benefiting from the powerful expressive capability of graphs, graph-based approaches have been popularly applied to handle multi-modal medical data and achieved impressive performa…
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning
Zhenyu Guo, Shuai Zheng, Zhizhe Liu +2
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but…
Multi-modal Graph Learning for Disease Prediction
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +3
Benefiting from the powerful expressive capability of graphs, graph-based approaches have achieved impressive performance in various biomedical applications. Most existing methods…
Adversarial Graph Disentanglement
Shuai Zheng, Zhenfeng Zhu, Zhizhe Liu +2
A real-world graph has a complex topological structure, which is often formed by the interaction of different latent factors. However, most existing methods lack consideration of t…
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
Shuai Zheng, Zhenfeng Zhu, Xingxing Zhang +3
Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks. However, most existing algorit…
Convolutional Prototype Learning for Zero-Shot Recognition
Zhizhe Liu, Xingxing Zhang, Zhenfeng Zhu +3
Zero-shot learning (ZSL) has received increasing attention in recent years especially in areas of fine-grained object recognition, retrieval, and image captioning. The key to ZSL i…
EA-LSTM: Evolutionary Attention-based LSTM for Time Series Prediction
Youru Li, Zhenfeng Zhu, Deqiang Kong +2
Time series prediction with deep learning methods, especially long short-term memory neural networks (LSTMs), have scored significant achievements in recent years. Despite the fact…