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Author:   Honghao Gao ,  Xinheng Wang
Publisher:   Springer International Publishing AG
Imprint:   Springer International Publishing AG
Volume:   626
ISBN:  

9783031932564

ISBN 10:   3031932560
Pages:   320
Publication Date:   10 August 2025
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Paperback
Publisher's Status:   Active
Availability:   Manufactured on demand  
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Table of Contents

Graph neural networks & Recommendation systems.- Time-aware Recommendations with Motif-Enhanced Graph Learning.- Spatial-Temporal Graph Attention Networks Based on Novel Adjacency Matrix For Weather Forecasting.- Repository-Level Code Generation Method Enhanced by Context-Dependent Graph Retrieval.- DGSR: Dual-Graph Sequential Recommendation with Gated and Heterogeneous GNNs.- Disentanglement-enhanced User Representation via Domain-level Clusters for Cross-Domain Recommendation.- Adaptive Web API Recommendation via Matching Service Clusters and Mashup Requirement.- Multi-channel Heterogeneous Graph Transformer based Unsupervised Anomaly Detection Model for IoT Time Series.- CBR-FIF: A Novel Dynamic Graph Node Embedding Computation Framework.- KG-ASI: A Knowledge Graph Enhanced Model-based Retriever for Document Retrieval.- Federated Learning and application.- Free-rider Attack Based on Data-free Knowledge Distillation in Federated Learning.- Client-Oriented Energy Optimization in Clustered Federated Learning with Model Partition.- FedUDA: Towards a Novel Unfairness Distribution Attack against Federated Learning Models.- Mal-GAT: A Method to Enhance Malware Traffic Detection with Graph Attention Networks.- A Federated Learning Framework with Blockchain and Cache Pools for Unreliable Devices in a Cloud-Edge-End Environment.- Model Similarity based Clustering Federated Learning in Edge Computing.- A Privacy-Preserving Edge Caching Algorithm Based on Permissioned Blockchain and Federated Reinforcement Learning.

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