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Full Description
Graph Neural Networks (GNNs) have revolutionized the way we learn representations from graph-structured data, becoming a cornerstone for applications in social networks, recommendation systems, biology, and beyond. However, mainstream GNNs rely heavily on message passing, an iterative process of propagating information between connected nodes. While powerful, this method often incurs significant computational costs, making efficient training a growing challenge as graph sizes scale up.
This book addresses these challenges by offering a comprehensive exploration of efficient GNN training through the lens of data management. It highlights how innovative techniques, rooted in decades of graph processing research, can optimize the entire training process without compromising performance. By focusing on system-level enhancements and practical solutions, it provides actionable strategies to overcome efficiency bottlenecks in large-scale GNN training.
Readers will gain a deeper understanding of the graph data lifecycle in GNN training, with examples that demonstrate how data management techniques can significantly enhance scalability and performance. The book is designed for a broad audience, including students, researchers, and professionals, offering clear explanations and practical insights for anyone looking to master efficient GNN training.
Contents
"Chapter 1. Introduction".- "Chapter 2. Graph Neural Network Training Systems".- "Chapter 3. Reducing Data Transfer for Scalable GNN Training".- "Chapter 4. Accelerating Computation for Scalable GNN Training".- "Chapter 5. Distributed GNN Training".- "Chapter 6. Scalable Temporal GNN Training on Dynamic Graphs".
