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Paperback. Zustand: new. Paperback. This book constitutes the refereed proceedings of the 31st China Conference on Information Retrieval, CCIR 2025, held in Wuhan, China, during August 1517, 2025.The 9 full papers were presented in this volume were carefully reviewed and selected from 13 submissions. This conference focuses on the deep integration of large language models with classic retrieval paradigms. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

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Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book constitutes the refereed proceedings of the 31st China Conference on Information Retrieval, CCIR 2025, held in Wuhan, China, during August 15 17, 2025.The 9 full papers were presented in this volume were carefully reviewed and selected from 13 submissions. This conference focuses on the deep integration of large language models with classic retrieval paradigms.…

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Taschenbuch. Zustand: Neu. Information Retrieval | 31st China Conference, CCIR 2025, Shihezi, China, August 15-17, 2025, Revised Selected Papers | Zhigang Li (u. a.) | Taschenbuch | Lecture Notes in Computer Science | xi | Englisch | 2026 | Springer | EAN 9789819556366 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.…

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Hardback. Zustand: New. 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.…

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Buch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. …

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