4 papers
Scalable and Efficient Joint Spiking Embedding Predictive Architecture for Large-Scale Dynamic Graphs
Huizhe Zhang, Yuchang Zhu, Huazhen Zhong +2
Dynamic graph learning aims to capture evolving structural and semantic patterns in real-world systems, such as fraud detection and recommender systems. Due to the scarcity of labe…
Grad2Fair: A Gradient-driven Approach for Graph Fairness without Demographics
Yuchang Zhu, Zezhong Xie, Huizhe Zhang +4
Graph neural networks (GNNs) frequently encounter group fairness issues, often yielding biased predictions against specific demographic groups defined by sensitive attributes such…
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning
Yuchang Zhu, Huazhen Zhong, Qunshu Lin +6
With the remarkable generative capabilities of large language models (LLMs), using LLM-generated data to train downstream models has emerged as a promising approach to mitigate dat…
GT-SNT: A Linear-Time Transformer for Large-Scale Graphs via Spiking Node Tokenization
Huizhe Zhang, Jintang Li, Yuchang Zhu +2
Graph Transformers (GTs), which integrate message passing and self-attention mechanisms simultaneously, have achieved promising empirical results in graph prediction tasks. However…