Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack.
IEEE Trans. Knowl. Data Eng., July, 2025
DropNaE: Alleviating irregularity for large-scale graph representation learning.
Neural Networks, 2025
MoDSE: A High-Accurate Multiobjective Design Space Exploration Framework for CPU Microarchitectures.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., May, 2024
Revisiting Edge Perturbation for Graph Neural Network in Graph Data Augmentation and Attack.
CoRR, 2024
Disttack: Graph Adversarial Attacks Toward Distributed GNN Training.
Proceedings of the Euro-Par 2024: Parallel Processing, 2024
A Survey of Graph Pre-processing Methods: From Algorithmic to Hardware Perspectives.
CoRR, 2023
A High-accurate Multi-objective Exploration Framework for Design Space of CPU.
Proceedings of the 60th ACM/IEEE Design Automation Conference, 2023
General spiking neural network framework for the learning trajectory from a noisy mmWave radar.
Neuromorph. Comput. Eng., June, 2022
Sampling Methods for Efficient Training of Graph Convolutional Networks: A Survey.
IEEE CAA J. Autom. Sinica, 2022
Rethinking Efficiency and Redundancy in Training Large-scale Graphs.
CoRR, 2022
GNNSampler: Bridging the Gap Between Sampling Algorithms of GNN and Hardware.
Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022
Survey on Graph Neural Network Acceleration: An Algorithmic Perspective.
Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022
ENAS4D: Efficient Multi-stage CNN Architecture Search for Dynamic Inference.
CoRR, 2020