Research map / Graph learning / GNN architectures
Scalable and Effective Implicit Graph Neural Networks on Large Graphs
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Proposes a scalable implicit GNN framework for large graphs that addresses the cost of full-batch training and many fixed-point iterations.
Problem
Implicit GNNs capture long-range dependencies well but are computationally expensive and memory-intensive on large graphs due to full-batch training and many iterations needed to reach a fixed point.
Approach
Makes implicit GNNs scalable by moving away from full-batch training and reducing the number of fixed-point iterations required, while retaining effectiveness on long-range dependencies.
Closest papers by meaning
- Implicit Graph Neural Networks (NeurIPS 2020 · similarity 0.91)
- A Comprehensive Study on Large-Scale Graph Training: Benchmarking and Rethinking (NeurIPS 2022 · similarity 0.91)
- Optimization of Graph Neural Networks: Implicit Acceleration by Skip Connections and More Depth (ICML 2021 · similarity 0.90)
- Generative Graph Pattern Machine (NeurIPS 2025 · similarity 0.88)
- Diffusing to the Top: Boost Graph Neural Networks with Minimal Hyperparameter Tuning (ICLR 2025 · similarity 0.88)
- Implicit Subgraph Neural Network (ICML 2025 · similarity 0.87)
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