Scalability & Efficiency

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
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. …
Example Searcher: A Spatial Query System via Example
Location search in spatial services such as online maps nowadays is usually based on criteria filtering methods. Due to its algorithmic …
Example Searcher: A Spatial Query System via Example
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
Achieving Adversarial Robustness via Sparsity
Network pruning has been known to produce compact models without much accuracy degradation. However, how the pruning process affects a …
Ningyi Liao, Shufan Wang, Liyao Xiang, Nanyang Ye, Shuo Shao, Pengzhi Chu
Achieving Adversarial Robustness via Sparsity