3 papers
cs.LG2025
BLISS: Bandit Layer Importance Sampling Strategy for Efficient Training of Graph Neural Networks
Omar Alsaqa, Linh Thi Hoang, Muhammed Fatih Balin
Graph Neural Networks (GNNs) are powerful tools for learning from graph-structured data, but their application to large graphs is hindered by computational costs. The need to proce…
cs.LG2024
Cooperative Minibatching in Graph Neural Networks
Muhammed Fatih Balin, Dominique LaSalle, Ãmit V. Ãatalyürek
Training large scale Graph Neural Networks (GNNs) requires significant computational resources, and the process is highly data-intensive. One of the most effective ways to reduce r…
cs.LG2024
A Scalable and Effective Alternative to Graph Transformers
Kaan Sancak, Zhigang Hua, Jin Fang +5
Graph Neural Networks (GNNs) have shown impressive performance in graph representation learning, but they face challenges in capturing long-range dependencies due to their limited…