{"total":31,"rows":[{"id":"2205.07424","title":"Trustworthy Graph Neural Networks: Aspects, Methods and Trends","created":"2022-05-16","cited_by_count":89,"area":null},{"id":"2111.05193","title":"A Survey on Green Deep Learning","created":"2021-11-08","cited_by_count":52,"area":null},{"id":"2110.06804","title":"A comprehensive review of Binary Neural Network","created":"2021-10-11","cited_by_count":45,"area":null},{"id":"2111.06061","title":"Edge-Cloud Polarization and Collaboration: A Comprehensive Survey for AI","created":"2021-11-11","cited_by_count":39,"area":null},{"id":"2211.00216","title":"Distributed Graph Neural Network Training: A Survey","created":"2022-11-01","cited_by_count":32,"area":null},{"id":"2211.07091","title":"BiViT: Extremely Compressed Binary Vision Transformer","created":"2022-11-14","cited_by_count":28,"area":null},{"id":"2202.04822","title":"Survey on Graph Neural Network Acceleration: An Algorithmic Perspective","created":"2022-02-10","cited_by_count":26,"area":null},{"id":"2109.12872","title":"Meta-Aggregator: Learning to Aggregate for 1-bit Graph Neural Networks","created":"2021-09-27","cited_by_count":25,"area":null},{"id":"2111.09547","title":"QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core","created":"2021-11-18","cited_by_count":18,"area":null},{"id":"2207.08629","title":"Comprehensive Graph Gradual Pruning for Sparse Training in Graph Neural Networks","created":"2022-07-18","cited_by_count":18,"area":null},{"id":"2306.14052","title":"A Survey on Graph Neural Network Acceleration: Algorithms, Systems, and Customized Hardware","created":"2023-06-24","cited_by_count":18,"area":null},{"id":"2302.02922","title":"Joint Edge-Model Sparse Learning is Provably Efficient for Graph Neural Networks","created":"2023-02-06","cited_by_count":16,"area":null},{"id":"2305.19306","title":"A Graph is Worth 1-bit Spikes: When Graph Contrastive Learning Meets Spiking Neural Networks","created":"2023-05-30","cited_by_count":6,"area":null},{"id":"2308.14949","title":"Low-bit Quantization for Deep Graph Neural Networks with Smoothness-aware Message Propagation","created":"2023-08-29","cited_by_count":5,"area":null},{"id":"2207.14696","title":"BiFeat: Supercharge GNN Training via Graph Feature Quantization","created":"2022-07-29","cited_by_count":4,"area":null},{"id":"2208.03559","title":"Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy","created":"2022-08-06","cited_by_count":4,"area":null},{"id":"2305.02522","title":"BitGNN: Unleashing the Performance Potential of Binary Graph Neural Networks on GPUs","created":"2023-05-04","cited_by_count":4,"area":null},{"id":"2308.00890","title":"Tango: rethinking quantization for graph neural network training on GPUs","created":"2023-08-02","cited_by_count":4,"area":null},{"id":"2311.09775","title":"MEGA: A Memory-Efficient GNN Accelerator Exploiting Degree-Aware Mixed-Precision Quantization","created":"2023-11-16","cited_by_count":4,"area":null},{"id":"2302.00193","title":"$\\rm A^2Q$: Aggregation-Aware Quantization for Graph Neural Networks","created":"2023-02-01","cited_by_count":3,"area":null},{"id":"2310.15128","title":"Projected Stochastic Gradient Descent with Quantum Annealed Binary Gradients","created":"2023-10-23","cited_by_count":3,"area":null},{"id":"2312.05479","title":"Exploring Sparsity in Graph Transformers","created":"2023-12-09","cited_by_count":3,"area":null},{"id":"2505.09361","title":"Efficient Mixed Precision Quantization in Graph Neural Networks","created":"2025-05-14","cited_by_count":3,"area":null},{"id":"2112.05215","title":"Road Extraction from Overhead Images with Graph Neural Networks","created":"2021-12-09","cited_by_count":2,"area":null},{"id":"2210.03900","title":"Bottleneck Analysis of Dynamic Graph Neural Network Inference on CPU and GPU","created":"2022-10-08","cited_by_count":2,"area":null},{"id":"2403.13268","title":"Unifews: You Need Fewer Operations for Efficient Graph Neural Networks","created":"2024-03-20","cited_by_count":2,"area":null},{"id":"2309.03523","title":"DGC: Training Dynamic Graphs with Spatio-Temporal Non-Uniformity using Graph Partitioning by Chunks","created":"2023-09-07","cited_by_count":1,"area":null},{"id":"2311.01205","title":"Attacking Graph Neural Networks with Bit Flips: Weisfeiler and Lehman Go Indifferent","created":"2023-11-02","cited_by_count":0,"area":null},{"id":"2405.04114","title":"Acceleration Algorithms in GNNs: A Survey","created":"2024-05-07","cited_by_count":0,"area":null},{"id":"2406.17611","title":"Distributed Training of Large Graph Neural Networks with Variable Communication Rates","created":"2024-06-25","cited_by_count":0,"area":null},{"id":"2608.30394","title":"TopGQ: Fast GNN Post-Training Quantization Leveraging Topology Information","created":"2026-08-31","cited_by_count":0,"area":null}],"next_cursor":null}