1 code implementation • 19 Oct 2023 • Muhammed Fatih Balin, Dominique LaSalle, Ümit V. Çatalyürek
Significant computational resources are required to train Graph Neural Networks (GNNs) at a large scale, and the process is highly data-intensive.
1 code implementation • 24 Oct 2022 • Muhammed Fatih Balin, Ümit V. Çatalyürek
It is designed to be a direct replacement for Neighbor Sampling (NS) with the same fanout hyperparameter while sampling up to 7 times fewer vertices, without sacrificing quality.
1 code implementation • 17 Oct 2021 • Muhammed Fatih Balin, Kaan Sancak, Ümit V. Çatalyürek
Full batch training of Graph Convolutional Network (GCN) models is not feasible on a single GPU for large graphs containing tens of millions of vertices or more.
1 code implementation • 16 Sep 2020 • Abdurrahman Yaşar, Muhammed Fatih Balin, Xiaojing An, Kaan Sancak, Ümit V. Çatalyürek
More specifically, in this work, we address the problem of symmetric rectilinear partitioning of a square matrix.
Data Structures and Algorithms