Tag: Causal Graph Neural Networks
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LLM and Graph Integration Insights from VLDB 2025
Explore key research, challenges, and innovations from the LLM+Graph Workshop at VLDB 2025 on merging large language models with graph data.
ScaleGNN: Scalable Mini-batch GNN Training with 4D Parallelism
Discover ScaleGNN's communication-free sampling and 4D hybrid parallelism for efficient, scalable mini-batch training of graph neural networks on large dat...
Analytic Drift Resister for Improved Continual Graph Learning
Discover how Analytic Drift Resister enhances Non-Exemplar Continual Graph Learning by preventing feature drift and boosting model adaptability.
Lightweight Graph Neural Network for Vulnerability Detection
Discover VulGNN, a lightweight graph neural network that detects software vulnerabilities efficiently with high accuracy and low resource use.
GSR-GNN: Fast, Memory-Efficient Deep GNNs for Circuit Graphs
Discover GSR-GNN, a framework that accelerates training and saves memory for deep GNNs on large circuit graphs, boosting efficiency and scalability.