9 citations · 21 across the 20 of their papers we have counts for
7 papers · 1 filter
Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs
Taiyan Zhang, Renchi Yang, Yurui Lai +3
Graph neural networks (GNNs) have become the preferred models for node classification in graph data due to their robust capabilities in integrating graph structures and attributes.…
Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need
Runzhen Xue, Hao Wu, Mingyu Yan +4
Design Space Exploration (DSE) is essential to modern CPU design, yet current frameworks struggle to scale and generalize in high-dimensional architectural spaces. As the dimension…
Characterizing and Understanding HGNN Training on GPUs
Dengke Han, Mingyu Yan, Xiaochun Ye +1
Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world…
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training
Yuxiang Zhang, Xin Liu, Meng Wu +4
Graph Neural Networks (GNNs) have emerged as potent models for graph learning. Distributing the training process across multiple computing nodes is the most promising solution to a…
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack
Xin Liu, Yuxiang Zhang, Meng Wu +6
Edge perturbation is a basic method to modify graph structures. It can be categorized into two veins based on their effects on the performance of graph neural networks (GNNs), i.e.…
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective
Xin Liu, Mingyu Yan, Lei Deng +5
Graph neural networks (GNNs) have been a hot spot of recent research and are widely utilized in diverse applications. However, with the use of huger data and deeper models, an urge…