Acceleration Algorithms in GNNs: A Survey
Fuente:
arXiv
Saved in:
| Main Authors: | Ma, Lu, Sheng, Zeang, Li, Xunkai, Gao, Xinyi, Hao, Zhezheng, Yang, Ling, Zhang, Wentao, Cui, Bin |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Towards Scalable and Deep Graph Neural Networks via Noise Masking
by: Liang, Yuxuan, et al.
Published: (2024)
by: Liang, Yuxuan, et al.
Published: (2024)
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
by: Yang, Ling, et al.
Published: (2023)
by: Yang, Ling, et al.
Published: (2023)
Graph Condensation: A Survey
by: Gao, Xinyi, et al.
Published: (2024)
by: Gao, Xinyi, et al.
Published: (2024)
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
by: Gao, Xinyi, et al.
Published: (2024)
by: Gao, Xinyi, et al.
Published: (2024)
Diffusion Models: A Comprehensive Survey of Methods and Applications
by: Yang, Ling, et al.
Published: (2022)
by: Yang, Ling, et al.
Published: (2022)
How Hard Is It for Message-Passing GNNs to Simulate One Weisfeiler-Lehman Color-Refinement Step?
by: Cui, Guanyu, et al.
Published: (2024)
by: Cui, Guanyu, et al.
Published: (2024)
GSR-GNN: Training Acceleration and Memory-Saving Framework of Deep GNNs on Circuit Graph
by: Luo, Yuebo, et al.
Published: (2026)
by: Luo, Yuebo, et al.
Published: (2026)
Using Subgraph GNNs for Node Classification:an Overlooked Potential Approach
by: Zeng, Qian, et al.
Published: (2025)
by: Zeng, Qian, et al.
Published: (2025)
Empowering GNNs via Edge-Aware Weisfeiler-Leman Algorithm
by: Liu, Meng, et al.
Published: (2022)
by: Liu, Meng, et al.
Published: (2022)
DSO: Dual-Scale Neural Operators for Stable Long-term Fluid Dynamics Forecasting
by: Dong, Huanshuo, et al.
Published: (2026)
by: Dong, Huanshuo, et al.
Published: (2026)
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
by: Attali, Hugo, et al.
Published: (2024)
by: Attali, Hugo, et al.
Published: (2024)
Graph Rewiring in GNNs to Mitigate Over-Squashing and Over-Smoothing: A Survey
by: Attali, Hugo, et al.
Published: (2026)
by: Attali, Hugo, et al.
Published: (2026)
ConstellationNet: Reinventing Spatial Clustering through GNNs
by: Gao, Aidan, et al.
Published: (2025)
by: Gao, Aidan, et al.
Published: (2025)
GraphBridge: Towards Arbitrary Transfer Learning in GNNs
by: Ju, Li, et al.
Published: (2025)
by: Ju, Li, et al.
Published: (2025)
Federated Continual Learning via Knowledge Fusion: A Survey
by: Yang, Xin, et al.
Published: (2023)
by: Yang, Xin, et al.
Published: (2023)
DARTS: Distribution-Aware Active Rollout Trajectory Shaping for Accelerating LLM Reinforcement Learning
by: Wang, Yujie, et al.
Published: (2026)
by: Wang, Yujie, et al.
Published: (2026)
LEPO: Latent Reasoning Policy Optimization for Large Language Models
by: Zhou, Yuyan, et al.
Published: (2026)
by: Zhou, Yuyan, et al.
Published: (2026)
Learning What Reinforcement Learning Can't: Interleaved Online Fine-Tuning for Hardest Questions
by: Ma, Lu, et al.
Published: (2025)
by: Ma, Lu, et al.
Published: (2025)
Graph Learning in the Era of LLMs: A Survey from the Perspective of Data, Models, and Tasks
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
Rethinking Node-wise Propagation for Large-scale Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
Rethinking Federated Graph Learning: A Data Condensation Perspective
by: Zhang, Hao, et al.
Published: (2025)
by: Zhang, Hao, et al.
Published: (2025)
Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy
by: Xue, Ruizhan, et al.
Published: (2025)
by: Xue, Ruizhan, et al.
Published: (2025)
Neural P$^3$M: A Long-Range Interaction Modeling Enhancer for Geometric GNNs
by: Wang, Yusong, et al.
Published: (2024)
by: Wang, Yusong, et al.
Published: (2024)
AdaFGL: A New Paradigm for Federated Node Classification with Topology Heterogeneity
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
The Heterophilic Snowflake Hypothesis: Training and Empowering GNNs for Heterophilic Graphs
by: Wang, Kun, et al.
Published: (2024)
by: Wang, Kun, et al.
Published: (2024)
Transformer in Touch: A Survey
by: Gao, Jing, et al.
Published: (2024)
by: Gao, Jing, et al.
Published: (2024)
State Space Models over Directed Graphs
by: She, Junzhi, et al.
Published: (2025)
by: She, Junzhi, et al.
Published: (2025)
LightDiC: A Simple yet Effective Approach for Large-scale Digraph Representation Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
LLM Enhancers for GNNs: An Analysis from the Perspective of Causal Mechanism Identification
by: Gao, Hang, et al.
Published: (2025)
by: Gao, Hang, et al.
Published: (2025)
Enhancing GNNs with Architecture-Agnostic Graph Transformations: A Systematic Analysis
by: Li, Zhifei, et al.
Published: (2024)
by: Li, Zhifei, et al.
Published: (2024)
Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One
by: Zhang, Hongyuan, et al.
Published: (2023)
by: Zhang, Hongyuan, et al.
Published: (2023)
Learning from History: A Retrieval-Augmented Framework for Spatiotemporal Prediction
by: Jia, Hao, et al.
Published: (2025)
by: Jia, Hao, et al.
Published: (2025)
On the Expressive Power of GNNs for Boolean Satisfiability
by: Peltonen, Saku, et al.
Published: (2026)
by: Peltonen, Saku, et al.
Published: (2026)
Unsupervised Optimisation of GNNs for Node Clustering
by: Leeney, William, et al.
Published: (2024)
by: Leeney, William, et al.
Published: (2024)
FedGTA: Topology-aware Averaging for Federated Graph Learning
by: Li, Xunkai, et al.
Published: (2024)
by: Li, Xunkai, et al.
Published: (2024)
TGB-Seq Benchmark: Challenging Temporal GNNs with Complex Sequential Dynamics
by: Yi, Lu, et al.
Published: (2025)
by: Yi, Lu, et al.
Published: (2025)
SFR-GNN: Simple and Fast Robust GNNs against Structural Attacks
by: Ai, Xing, et al.
Published: (2024)
by: Ai, Xing, et al.
Published: (2024)
Benchmarking Positional Encodings for GNNs and Graph Transformers
by: Grötschla, Florian, et al.
Published: (2024)
by: Grötschla, Florian, et al.
Published: (2024)
Binarizing Physics-Inspired GNNs for Combinatorial Optimization
by: Krutský, Martin, et al.
Published: (2025)
by: Krutský, Martin, et al.
Published: (2025)
Can LLMs be Good Graph Judge for Knowledge Graph Construction?
by: Huang, Haoyu, et al.
Published: (2024)
by: Huang, Haoyu, et al.
Published: (2024)
Similar Items
-
Towards Scalable and Deep Graph Neural Networks via Noise Masking
by: Liang, Yuxuan, et al.
Published: (2024) -
VQGraph: Rethinking Graph Representation Space for Bridging GNNs and MLPs
by: Yang, Ling, et al.
Published: (2023) -
Graph Condensation: A Survey
by: Gao, Xinyi, et al.
Published: (2024) -
Rethinking and Accelerating Graph Condensation: A Training-Free Approach with Class Partition
by: Gao, Xinyi, et al.
Published: (2024) -
Diffusion Models: A Comprehensive Survey of Methods and Applications
by: Yang, Ling, et al.
Published: (2022)