11 citations · 15 across the 6 of their papers we have counts for
6 papers
Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs
Tiehua Zhang, Yuze Liu, Zhishu Shen +4
Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learni…
Towards Relation-centered Pooling and Convolution for Heterogeneous Graph Learning Networks
Tiehua Zhang, Yuze Liu, Yao Yao +4
Heterogeneous graph neural network has unleashed great potential on graph representation learning and shown superior performance on downstream tasks such as node classification and…
An Adaptive Federated Relevance Framework for Spatial Temporal Graph Learning
Tiehua Zhang, Yuze Liu, Zhishu Shen +4
Spatial-temporal data contains rich information and has been widely studied in recent years due to the rapid development of relevant applications in many fields. For instance, medi…
GPS: A Policy-driven Sampling Approach for Graph Representation Learning
Tiehua Zhang, Yuze Liu, Xin Chen +3
Graph representation learning has drawn increasing attention in recent years, especially for learning the low dimensional embedding at both node and graph level for classification…
AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees
Rong Liang, Tiehua Zhang, Yujie Lu +3
Using the pre-trained language models to understand source codes has attracted increasing attention from financial institutions owing to the great potential to uncover financial ri…
STFL: A Temporal-Spatial Federated Learning Framework for Graph Neural Networks
Guannan Lou, Yuze Liu, Tiehua Zhang +1
We present a spatial-temporal federated learning framework for graph neural networks, namely STFL. The framework explores the underlying correlation of the input spatial-temporal d…