Graph Algorithms

Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
We propose LD² as a scalable heterophilous GNN with linear complexity and lightweight minibatch training.
Jul 31, 2024 14:45 — 15:00 SMU, Singapore
Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs
A GPU-oriented subgraph representation learning algorithm on dynamic data.
GENTI: GPU-powered Walk-based Subgraph Extraction for Scalable Representation Learning on Dynamic Graphs
Unifews: Unified Entry-Wise Sparsification for Efficient Graph Neural Network
Graph Neural Networks (GNNs) have shown promising performance in various graph learning tasks, but at the cost of resource-intensive …
Scalable Decoupling Graph Neural Networks with Feature-Oriented Optimization
SCARA extension with a more scalable reuse scheme.
Scalable Decoupling Graph Neural Networks with Feature-Oriented Optimization
LD²: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
A scalable heterophilous GNN with lightweight minibatch training.
LD²: Scalable Heterophilous Graph Neural Network with Decoupled Embeddings
Machine Learning for Subgraph Extraction: Methods, Applications and Challenges
Subgraphs are obtained by extracting a subset of vertices and a sub- set of edges from the associated original graphs, and many graph …
Machine Learning for Subgraph Extraction: Methods, Applications and Challenges
Machine Learning for Subgraph Extraction: Methods, Applications and Challenges – Community Detection & Maximum Common Subgraph
Subgraphs are obtained by extracting a subset of vertices and a sub- set of edges from the associated original graphs, and many graph …
Aug 31, 2023 10:30 — 12:00 Vancouver, Canada
Machine Learning for Subgraph Extraction: Methods, Applications and Challenges – Community Detection & Maximum Common Subgraph
Scaling up Graph Neural Networks
Qualifying Examinations report.
Scaling up Graph Neural Networks
SIMGA: A Simple and Effective Heterophilous Graph Neural Network with Efficient Global Aggregation
Graph neural networks (GNNs) realize great success in graph learning but suffer from performance loss when meeting heterophily, i.e. …
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization
A feature-oriented GNN that scales to billion-scale extremely large graphs.
SCARA: Scalable Graph Neural Networks with Feature-Oriented Optimization