Database Systems for Advanced Applications: 30th International Conference, DASFAA 2025, Singapore, Singapore, May 26–29, 2025, Proceedings, Part III
Author:   Feida Zhu ,  Philip S. Yu ,  Akiyo Nadamoto ,  Ee-Peng Lim
Publisher:   Springer Verlag, Singapore
ISBN:  

9789819539055


Pages:   509
Publication Date:   03 January 2026
Format:   Paperback
Availability:   In Print   Availability explained
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Database Systems for Advanced Applications: 30th International Conference, DASFAA 2025, Singapore, Singapore, May 26–29, 2025, Proceedings, Part III


Overview

This six-volume set LNCS 15986-15991 constitutes the proceedings of the 30th International Conference on Database Systems for Advanced Applications, DASFAA 2025, held in Singapore, during May 26–29, 2025. The 136 full papers presented in this book together with 89 short papers were carefully reviewed and selected from 731 submissions.They cover topics such as Part I- Machine Learning and Text. Part II- Emerging Application; NLP and Spatial-Temporal. Part III- Graph; Knowledge Graph. Part V- Recommendation and Security & Privacy. Part VI- Language Model; Industry Papers and Demo Papers.

Full Product Details

Author:   Feida Zhu ,  Philip S. Yu ,  Akiyo Nadamoto ,  Ee-Peng Lim
Publisher:   Springer Verlag, Singapore
Imprint:   Springer Verlag, Singapore
ISBN:  

9789819539055


ISBN 10:   9819539056
Pages:   509
Publication Date:   03 January 2026
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   In Print   Availability explained
This item will be ordered in for you from one of our suppliers. Upon receipt, we will promptly dispatch it out to you. For in store availability, please contact us.

Table of Contents

.- Graph. .- Scalable GNN Training via Parameter Freeze and Layer Detachment. .- Dual-prompting based Event Anomaly Detection in Dynamic Graphs. .- STM: A Spatio-Temporal Model for Dynamic Graph Fraud Detection. .- ICFF-Net: Interlaced cross-attention feature fusion network for music genre classification. .- Structure-aware Self-supervised Graph Representation Learning. .- NodeNAS: Node-Specific Graph Neural Architecture Search for Out-of-Distribution Generalization. .- Efficient Maximum (α, β)-Quasi Biclique Computation on Bipartite Graphs.

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