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Book Synopsis

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.

Collaborative Computing Networking Applications and Worksharing

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    Order before 4pm today for delivery by Mon 15 Jun 2026.

    A Paperback by Honghao Gao

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      View other formats and editions of Collaborative Computing Networking Applications and Worksharing by Honghao Gao

      Publisher: Springer
      Publication Date: 09/09/2025
      ISBN13: 9783031932564, 978-3031932564
      ISBN10:
      Also in:
      Computing Systems analysis and design

      Description

      Book Synopsis

      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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