Aug 14, 2021 · In this paper, we propose a performance-adaptive sampling strategy PASS that samples neighbors informative for a target task.

Graph Convolutional Networks (GCNs) becomes a powerful deep learning tool for representation learning of graph data. • Adapting GCNs to large-scale ...

A performance-adaptive sampling strategy PASS that samples neighbors informative for a target task that outperforms state-of-the-art sampling methods by up ...

PASS: Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks. PASS is a neighborhood sampler for graph neural network models.

Aug 18, 2021 · In our extensive experiments, PASS outperforms state-of-the-art sampling methods by up to 10% accuracy on public benchmarks and up to 53% ...

Dec 3, 2021 · Here, we introduce a performance-adaptive sampling strategy for GCNs to solve both scalability and accuracy problems at once.

Node sampling mechanisms have been widely adopted in knowledge graph reasoning tasks based on graph neural networks [23] ; they are effective in improving the ...

Duration: 19:07
Posted: Jul 22, 2021

We evaluate the performance of our method on four popular benchmarks for node classification, including Cora, Citeseer, Pubmed [11] and Reddit [3]. Intensive ...

Performance-Adaptive Sampling Strategy Towards Fast and Accurate Graph Neural Networks. KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge ...