64 citations · 77 across the 5 of their papers we have counts for
5 papers
UniSAGE: Unifying Static and Dynamic Attributes with Hyper-Structure
Taoran Fang, Yan Deng, Chunping Wang +3
With the rapid growth of digital data, real-world applications increasingly involve hierarchical information that combines static attributes with dynamic records. Modeling such het…
KAA: Kolmogorov-Arnold Attention for Enhancing Attentive Graph Neural Networks
Taoran Fang, Tianhong Gao, Chunping Wang +4
Graph neural networks (GNNs) with attention mechanisms, often referred to as attentive GNNs, have emerged as a prominent paradigm in advanced GNN models in recent years. However, o…
Exploring Correlations of Self-Supervised Tasks for Graphs
Taoran Fang, Wei Zhou, Yifei Sun +3
Graph self-supervised learning has sparked a research surge in training informative representations without accessing any labeled data. However, our understanding of graph self-sup…
Universal Prompt Tuning for Graph Neural Networks
Taoran Fang, Yunchao Zhang, Yang Yang +2
In recent years, prompt tuning has sparked a research surge in adapting pre-trained models. Unlike the unified pre-training strategy employed in the language field, the graph field…
DropMessage: Unifying Random Dropping for Graph Neural Networks
Taoran Fang, Zhiqing Xiao, Chunping Wang +3
Graph Neural Networks (GNNs) are powerful tools for graph representation learning. Despite their rapid development, GNNs also face some challenges, such as over-fitting, over-smoot…