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Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part III / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part III / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (465 pages)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Data mining
Artificial intelligence
Application software
Computer systems
Education - Data processing
Computer vision
Data Mining and Knowledge Discovery
Artificial Intelligence
Computer and Information Systems Applications
Computer System Implementation
Computers and Education
Computer Vision
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 9789819608218
981960821X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organisation -- Contents - Part III -- Graph Mining -- Verifiable Graph-Based Approximate Nearest Neighbor Search -- 1 Introduction -- 2 Related Works -- 2.1 Graph-Based Approximate Nearest Neighbor Search -- 2.2 Verifiable Nearest Neighbor Search -- 3 Preliminaries -- 3.1 Hierarchical Clustering-Based Nearest Neighbor Graph -- 3.2 Guided Tree -- 3.3 Merkle Hash Tree (MHT) -- 3.4 The Threat Model -- 4 Our Scheme -- 4.1 Initialization Phase -- 4.2 Query Processing Phase -- 4.3 Verification Phase -- 5 Security Discussion -- 6 Experiments -- 6.1 Setup -- 6.2 Impact of k and Number of Queries on VO Size -- 6.3 Computational Overhead -- 7 Conclusion -- References -- Depth-Enhanced Contrast Attribute Graph Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Deep Graph Clustering -- 2.2 Graph Data Augmentation -- 3 Method -- 3.1 Notations -- 3.2 Deep Enhancement Module -- 3.3 Contrast Learning Module -- 3.4 Self-optimizing Module -- 3.5 Overall Objective -- 4 Experiments -- 4.1 Baseline Dataset -- 4.2 Baseline -- 4.3 Experimental Setup -- 4.4 Evaluation Metrics -- 4.5 Performance Comparison -- 4.6 Ablation Experiment -- 4.7 Sensitivity Analysis -- 4.8 Visualization -- 4.9 Conclusion -- References -- FCMH: Fast Cluster Multi-hop Model for Graph Fraud Detection -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Network -- 2.2 Graph Fraud Detection -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Random Cluster Subgraph Division -- 3.3 Multi-hop Neighbor Difference Aggregation -- 3.4 Downsampling and Optimization Objective -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Classification Performance -- 4.3 Time Efficiency -- 4.4 Ablation Study -- 5 Conclusion -- References -- Emotion Graph Augmentation for Detecting Fake News in Online Social Networks -- 1 Introduction -- 2 Related Work -- 2.1 Text Based Methods.
2.2 Graph Based Methods -- 2.3 Emotion Based Methods -- 3 Problem Statement -- 4 Methodology -- 4.1 Semantic Graph Construction -- 4.2 Emotion Graph Construction -- 4.3 Graph Augmentation with Adversarial Perturbations -- 4.4 Propagation of Semantics and Emotions -- 4.5 Fake News Detection -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Overall Performance -- 5.3 Ablation Study -- 5.4 Parameter Analysis -- 5.5 Case Study -- 6 Conclusion -- References -- BiF-AC: A Bidirectional Feedback Actor-Critic Framework for UAV-UGV Graph-Based Search and Rescue Operations -- 1 Introduction -- 2 UAV-UGV Coordination System Model -- 2.1 System Model -- 2.2 Problem Formulation -- 3 The Proposed Method -- 3.1 The Principles of Actor-Critic -- 3.2 The Bidirectional Feedback Actor-Critic Algorithm -- 4 Empirical Studies -- 4.1 Experimental Settings -- 4.2 Performance Evaluation -- 5 Conclusion -- References -- RWEM: An In-Memory Random Walk Based Node Embedding Framework on Multiplex User-Item Graphs -- 1 Introduction -- 2 Background and Related Work -- 2.1 Graph Theory -- 2.2 Stochastic Markov Process -- 2.3 Node Embedding -- 3 RWEM Framework -- 3.1 Embedding Input -- 3.2 Autocovariance-Based Similarity -- 4 Evaluation -- 4.1 Environment -- 4.2 Setup -- 4.3 Results -- 5 Conclusion -- References -- Feature-Aware Unsupervised Detection of Important Nodes in Graphs -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Problem Statement -- 3.2 Graph Convolutional Networks -- 4 Proposed Model -- 4.1 Feature-Aware Personalized PageRank -- 4.2 Model Architecture -- 4.3 Training Time Cost Analysis -- 5 Experiments -- 5.1 Node Classification -- 5.2 Active Learning -- 6 Conclusion and Future Work -- References -- HHP: A Hybrid Partitioner for Large-Scale Hypergraph -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Hybrid Hypergraph Partitioner.
4.1 Basic Algorithm -- 4.2 Improved Online Partition Algorithm -- 4.3 Hybrid Hypergraph Partitioner -- 5 Evaluation -- 5.1 Experimental Setup -- 5.2 Hypergraph Partitioning -- 5.3 Experimental on MinMax++ -- 5.4 Study on Hybrid Strategies -- 6 Conclusions -- References -- Graph Fusion Based Autoencoder for Node Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Deep Clustering -- 2.2 Autoencoder -- 3 Method -- 3.1 Graph Fusion -- 3.2 Representation Learning -- 3.3 Node Clustering -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Result Analysis -- 4.3 Ablation Study -- 4.4 Parameter Sensitivity Analysis -- 5 Conclusion -- References -- Regional Food Culture Preference Mining Based on Restaurant POI -- 1 Introduction -- 2 Related Work -- 3 Dataset Construction -- 4 Methods -- 4.1 Statistical Analysis -- 4.2 Community Detection -- 5 Study of Chinese Cuisines -- 5.1 Data Distribution -- 5.2 Geographical Factors -- 5.3 Economical Factors -- 5.4 Population Factors -- 6 Clustering Analysis -- 6.1 Experimental Setup -- 6.2 Overall Performance -- 6.3 Ablation Study -- 6.4 Hyperparameter Analysis -- 6.5 Visualization Analysis -- 7 Conclusions -- References -- Multi-task Learning of Heterogeneous Hypergraph Representations in LBSNs -- 1 Introduction -- 2 Model and Problem Formulation -- 3 Constructing the Heterogeneous Hypergraph -- 4 Heterogeneous Hypergraph Learning -- 4.1 Hypergraph Input -- 4.2 Adaptive Heterogeneous Hypergraph Convolutional Network -- 5 Multi-task Learning -- 6 Empirical Evaluation -- 6.1 End-to-End Comparison -- 6.2 Ablation Testing -- 6.3 Hyperparameter Sensitivity -- 7 Conclusion -- References -- Graph Contrastive Learning for Dissolved Gas Analysis -- 1 Introduction -- 2 Preliminaries -- 2.1 Notation -- 2.2 Constructing KNN Graph -- 3 Methodology -- 3.1 Dual-Channel Graph Representation Learning.
3.2 Ranking Contrastive Learning -- 3.3 Fault Detection -- 4 Experiment -- 4.1 Experimental Setup(RQ1) -- 4.2 Performance Comparison -- 4.3 Ablation Study(RQ2) -- 4.4 Parameter Analysis(RQ3) -- 5 Conclusion -- References -- GCS: A Graph-Augmented Semi-supervised Contrastive Learning Approach for Imbalanced Dissolved Gas Analysis in Power Transformers -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Imbalance Settings and Problem Definition -- 3 Methodology -- 3.1 Graph Construction -- 3.2 Semi-supervised Contrastive Learning -- 3.3 Graph Augmentation -- 3.4 Classification and Model Optimization -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Main Comparison Results (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Imbalance Comparison(RQ3) -- 5 Conclusion -- References -- Contrastive Learning Based on Bipartite Graphs for Interpretable Knowledge Tracing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Overview -- 3.3 Embedding and Knowledge Structure -- 3.4 Bipartite Graph Attention Network -- 3.5 Bipartite Graph Contrastive Learning -- 3.6 Prediction -- 3.7 Model Optimization -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Baselines and Experimental Settings -- 4.3 Implementation Details -- 4.4 Performance Analysis -- 4.5 Interpretability Discussion -- 4.6 Ablation Study -- 4.7 Conclusion -- References -- Graph Data Understanding and Interpretation Enabled by Large Language Models -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Heterogeneous Data Representation Learning -- 2.3 Converter Alignment Tuning -- 2.4 Retrieval Augmented Thoughts -- 3 Experiments -- 3.1 Training Details -- 3.2 Baseline Method -- 3.3 Performance Comparison -- 3.4 Ablation Experiments -- 4 Conclusion -- References -- SDM-GAT: StylisticFP Detection Method Based on Graph Attention Network -- 1 Introduction.
2 Background and Related Work -- 2.1 Tracking Development -- 2.2 Detection Methods -- 3 Methodology -- 3.1 Preliminaries -- 3.2 Graph Building -- 3.3 Graph Attention Network -- 4 Experiment -- 4.1 Datasets -- 4.2 Baselines and Metric -- 4.3 Implementation Details -- 4.4 Baseline Model Comparison -- 4.5 Impact of Graph Pruning -- 5 Conclusion -- References -- Anomaly Aligned Subgraphs Detection on Multi-layer Attributed Networks -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Network Alignment -- 3 Methodology -- 3.1 Anomaly Detection -- 3.2 Network Alignment -- 3.3 Update Anomaly Subgraph Node Set -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Case Study -- 5 Conclusion -- References -- Path-Aware Siamese Graph Neural Network for Link Prediction -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Formulation -- 3.2 Model Buildup -- 3.3 Contrastive Learning -- 4 Experiments -- 4.1 Datasets and Task -- 4.2 Baselines -- 4.3 Metrics and Settings -- 4.4 Abalation Study -- 5 Conclusion -- References -- GEM-GNN: Group Enhanced Multi-relation Graph Neural Networks for Fraud Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Problem Definition -- 3.2 Model Architecture -- 3.3 Neighbor Aggregation Module -- 3.4 Group-Based Aggregation Module -- 3.5 Optimization -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Comparison -- 4.3 Sensitivity Analysis -- 4.4 Training Process Study -- 5 Application -- 6 Conclusion -- References -- Spatial Data Mining -- ESNet: Perceptive Spatial-Spectral Fusion with Multi-stage Reconstruction for Pansharpening -- 1 Introduction -- 2 The Proposed Model -- 2.1 ESNet -- 2.2 Enhanced Spatial Spectral Attention Module -- 2.3 Multi-scale Reconstruction Module -- 2.4 Loss Function -- 3 Experiment -- 3.1 Datasets and Settings -- 3.2 Accuracy Evaluation.
3.3 Results.
Record Nr. UNINA-9910983306803321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part VI / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part VI / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (398 pages)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Data mining
Artificial intelligence
Application software
Computer systems
Education - Data processing
Computer vision
Data Mining and Knowledge Discovery
Artificial Intelligence
Computer and Information Systems Applications
Computer System Implementation
Computers and Education
Computer Vision
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 9789819608508
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910983039103321
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part VI / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part VI / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (398 pages)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Data mining
Artificial intelligence
Application software
Computer systems
Education - Data processing
Computer vision
Data Mining and Knowledge Discovery
Artificial Intelligence
Computer and Information Systems Applications
Computer System Implementation
Computers and Education
Computer Vision
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 9789819608508
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISA-996636772303316
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part III / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part III / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma
Edizione [1st ed. 2025.]
Pubbl/distr/stampa Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Descrizione fisica 1 online resource (465 pages)
Disciplina 006.3
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Data mining
Artificial intelligence
Application software
Computer systems
Education - Data processing
Computer vision
Data Mining and Knowledge Discovery
Artificial Intelligence
Computer and Information Systems Applications
Computer System Implementation
Computers and Education
Computer Vision
Mineria de dades
Soggetto genere / forma Congressos
Llibres electrònics
ISBN 9789819608218
981960821X
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intro -- Preface -- Organisation -- Contents - Part III -- Graph Mining -- Verifiable Graph-Based Approximate Nearest Neighbor Search -- 1 Introduction -- 2 Related Works -- 2.1 Graph-Based Approximate Nearest Neighbor Search -- 2.2 Verifiable Nearest Neighbor Search -- 3 Preliminaries -- 3.1 Hierarchical Clustering-Based Nearest Neighbor Graph -- 3.2 Guided Tree -- 3.3 Merkle Hash Tree (MHT) -- 3.4 The Threat Model -- 4 Our Scheme -- 4.1 Initialization Phase -- 4.2 Query Processing Phase -- 4.3 Verification Phase -- 5 Security Discussion -- 6 Experiments -- 6.1 Setup -- 6.2 Impact of k and Number of Queries on VO Size -- 6.3 Computational Overhead -- 7 Conclusion -- References -- Depth-Enhanced Contrast Attribute Graph Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Deep Graph Clustering -- 2.2 Graph Data Augmentation -- 3 Method -- 3.1 Notations -- 3.2 Deep Enhancement Module -- 3.3 Contrast Learning Module -- 3.4 Self-optimizing Module -- 3.5 Overall Objective -- 4 Experiments -- 4.1 Baseline Dataset -- 4.2 Baseline -- 4.3 Experimental Setup -- 4.4 Evaluation Metrics -- 4.5 Performance Comparison -- 4.6 Ablation Experiment -- 4.7 Sensitivity Analysis -- 4.8 Visualization -- 4.9 Conclusion -- References -- FCMH: Fast Cluster Multi-hop Model for Graph Fraud Detection -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Network -- 2.2 Graph Fraud Detection -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Random Cluster Subgraph Division -- 3.3 Multi-hop Neighbor Difference Aggregation -- 3.4 Downsampling and Optimization Objective -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Classification Performance -- 4.3 Time Efficiency -- 4.4 Ablation Study -- 5 Conclusion -- References -- Emotion Graph Augmentation for Detecting Fake News in Online Social Networks -- 1 Introduction -- 2 Related Work -- 2.1 Text Based Methods.
2.2 Graph Based Methods -- 2.3 Emotion Based Methods -- 3 Problem Statement -- 4 Methodology -- 4.1 Semantic Graph Construction -- 4.2 Emotion Graph Construction -- 4.3 Graph Augmentation with Adversarial Perturbations -- 4.4 Propagation of Semantics and Emotions -- 4.5 Fake News Detection -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Overall Performance -- 5.3 Ablation Study -- 5.4 Parameter Analysis -- 5.5 Case Study -- 6 Conclusion -- References -- BiF-AC: A Bidirectional Feedback Actor-Critic Framework for UAV-UGV Graph-Based Search and Rescue Operations -- 1 Introduction -- 2 UAV-UGV Coordination System Model -- 2.1 System Model -- 2.2 Problem Formulation -- 3 The Proposed Method -- 3.1 The Principles of Actor-Critic -- 3.2 The Bidirectional Feedback Actor-Critic Algorithm -- 4 Empirical Studies -- 4.1 Experimental Settings -- 4.2 Performance Evaluation -- 5 Conclusion -- References -- RWEM: An In-Memory Random Walk Based Node Embedding Framework on Multiplex User-Item Graphs -- 1 Introduction -- 2 Background and Related Work -- 2.1 Graph Theory -- 2.2 Stochastic Markov Process -- 2.3 Node Embedding -- 3 RWEM Framework -- 3.1 Embedding Input -- 3.2 Autocovariance-Based Similarity -- 4 Evaluation -- 4.1 Environment -- 4.2 Setup -- 4.3 Results -- 5 Conclusion -- References -- Feature-Aware Unsupervised Detection of Important Nodes in Graphs -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Problem Statement -- 3.2 Graph Convolutional Networks -- 4 Proposed Model -- 4.1 Feature-Aware Personalized PageRank -- 4.2 Model Architecture -- 4.3 Training Time Cost Analysis -- 5 Experiments -- 5.1 Node Classification -- 5.2 Active Learning -- 6 Conclusion and Future Work -- References -- HHP: A Hybrid Partitioner for Large-Scale Hypergraph -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 4 Hybrid Hypergraph Partitioner.
4.1 Basic Algorithm -- 4.2 Improved Online Partition Algorithm -- 4.3 Hybrid Hypergraph Partitioner -- 5 Evaluation -- 5.1 Experimental Setup -- 5.2 Hypergraph Partitioning -- 5.3 Experimental on MinMax++ -- 5.4 Study on Hybrid Strategies -- 6 Conclusions -- References -- Graph Fusion Based Autoencoder for Node Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Deep Clustering -- 2.2 Autoencoder -- 3 Method -- 3.1 Graph Fusion -- 3.2 Representation Learning -- 3.3 Node Clustering -- 4 Experiments -- 4.1 Experimental Setting -- 4.2 Result Analysis -- 4.3 Ablation Study -- 4.4 Parameter Sensitivity Analysis -- 5 Conclusion -- References -- Regional Food Culture Preference Mining Based on Restaurant POI -- 1 Introduction -- 2 Related Work -- 3 Dataset Construction -- 4 Methods -- 4.1 Statistical Analysis -- 4.2 Community Detection -- 5 Study of Chinese Cuisines -- 5.1 Data Distribution -- 5.2 Geographical Factors -- 5.3 Economical Factors -- 5.4 Population Factors -- 6 Clustering Analysis -- 6.1 Experimental Setup -- 6.2 Overall Performance -- 6.3 Ablation Study -- 6.4 Hyperparameter Analysis -- 6.5 Visualization Analysis -- 7 Conclusions -- References -- Multi-task Learning of Heterogeneous Hypergraph Representations in LBSNs -- 1 Introduction -- 2 Model and Problem Formulation -- 3 Constructing the Heterogeneous Hypergraph -- 4 Heterogeneous Hypergraph Learning -- 4.1 Hypergraph Input -- 4.2 Adaptive Heterogeneous Hypergraph Convolutional Network -- 5 Multi-task Learning -- 6 Empirical Evaluation -- 6.1 End-to-End Comparison -- 6.2 Ablation Testing -- 6.3 Hyperparameter Sensitivity -- 7 Conclusion -- References -- Graph Contrastive Learning for Dissolved Gas Analysis -- 1 Introduction -- 2 Preliminaries -- 2.1 Notation -- 2.2 Constructing KNN Graph -- 3 Methodology -- 3.1 Dual-Channel Graph Representation Learning.
3.2 Ranking Contrastive Learning -- 3.3 Fault Detection -- 4 Experiment -- 4.1 Experimental Setup(RQ1) -- 4.2 Performance Comparison -- 4.3 Ablation Study(RQ2) -- 4.4 Parameter Analysis(RQ3) -- 5 Conclusion -- References -- GCS: A Graph-Augmented Semi-supervised Contrastive Learning Approach for Imbalanced Dissolved Gas Analysis in Power Transformers -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Imbalance Settings and Problem Definition -- 3 Methodology -- 3.1 Graph Construction -- 3.2 Semi-supervised Contrastive Learning -- 3.3 Graph Augmentation -- 3.4 Classification and Model Optimization -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Main Comparison Results (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Imbalance Comparison(RQ3) -- 5 Conclusion -- References -- Contrastive Learning Based on Bipartite Graphs for Interpretable Knowledge Tracing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Overview -- 3.3 Embedding and Knowledge Structure -- 3.4 Bipartite Graph Attention Network -- 3.5 Bipartite Graph Contrastive Learning -- 3.6 Prediction -- 3.7 Model Optimization -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Baselines and Experimental Settings -- 4.3 Implementation Details -- 4.4 Performance Analysis -- 4.5 Interpretability Discussion -- 4.6 Ablation Study -- 4.7 Conclusion -- References -- Graph Data Understanding and Interpretation Enabled by Large Language Models -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Heterogeneous Data Representation Learning -- 2.3 Converter Alignment Tuning -- 2.4 Retrieval Augmented Thoughts -- 3 Experiments -- 3.1 Training Details -- 3.2 Baseline Method -- 3.3 Performance Comparison -- 3.4 Ablation Experiments -- 4 Conclusion -- References -- SDM-GAT: StylisticFP Detection Method Based on Graph Attention Network -- 1 Introduction.
2 Background and Related Work -- 2.1 Tracking Development -- 2.2 Detection Methods -- 3 Methodology -- 3.1 Preliminaries -- 3.2 Graph Building -- 3.3 Graph Attention Network -- 4 Experiment -- 4.1 Datasets -- 4.2 Baselines and Metric -- 4.3 Implementation Details -- 4.4 Baseline Model Comparison -- 4.5 Impact of Graph Pruning -- 5 Conclusion -- References -- Anomaly Aligned Subgraphs Detection on Multi-layer Attributed Networks -- 1 Introduction -- 2 Related Work -- 2.1 Anomaly Detection -- 2.2 Network Alignment -- 3 Methodology -- 3.1 Anomaly Detection -- 3.2 Network Alignment -- 3.3 Update Anomaly Subgraph Node Set -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Case Study -- 5 Conclusion -- References -- Path-Aware Siamese Graph Neural Network for Link Prediction -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Formulation -- 3.2 Model Buildup -- 3.3 Contrastive Learning -- 4 Experiments -- 4.1 Datasets and Task -- 4.2 Baselines -- 4.3 Metrics and Settings -- 4.4 Abalation Study -- 5 Conclusion -- References -- GEM-GNN: Group Enhanced Multi-relation Graph Neural Networks for Fraud Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Problem Definition -- 3.2 Model Architecture -- 3.3 Neighbor Aggregation Module -- 3.4 Group-Based Aggregation Module -- 3.5 Optimization -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Comparison -- 4.3 Sensitivity Analysis -- 4.4 Training Process Study -- 5 Application -- 6 Conclusion -- References -- Spatial Data Mining -- ESNet: Perceptive Spatial-Spectral Fusion with Multi-stage Reconstruction for Pansharpening -- 1 Introduction -- 2 The Proposed Model -- 2.1 ESNet -- 2.2 Enhanced Spatial Spectral Attention Module -- 2.3 Multi-scale Reconstruction Module -- 2.4 Loss Function -- 3 Experiment -- 3.1 Datasets and Settings -- 3.2 Accuracy Evaluation.
3.3 Results.
Record Nr. UNISA-996635667803316
Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Advanced Data Mining and Applications : 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings / / edited by Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng
Advanced Data Mining and Applications : 12th International Conference, ADMA 2016, Gold Coast, QLD, Australia, December 12-15, 2016, Proceedings / / edited by Jinyan Li, Xue Li, Shuliang Wang, Jianxin Li, Quan Z. Sheng
Edizione [1st ed. 2016.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Descrizione fisica 1 online resource (XVI, 817 p. 280 illus.)
Disciplina 006.312
Collana Lecture Notes in Artificial Intelligence
Soggetto topico Artificial intelligence
Data mining
Information storage and retrieval systems
Application software
Database management
Computer networks
Artificial Intelligence
Data Mining and Knowledge Discovery
Information Storage and Retrieval
Computer and Information Systems Applications
Database Management
Computer Communication Networks
ISBN 3-319-49586-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910484681803321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Computational intelligence for multimedia big data on the cloud with engineering applications / / edited by Arun Kumar Sangaiah, Michael Sheng, Zhiyong Zhang
Computational intelligence for multimedia big data on the cloud with engineering applications / / edited by Arun Kumar Sangaiah, Michael Sheng, Zhiyong Zhang
Edizione [First edition.]
Pubbl/distr/stampa London, United Kingdom : , : Academic Press, an imprint of Elsevier, , [2018]
Descrizione fisica 1 online resource (364 pages) : illustrations
Disciplina 006.3
Collana Intelligent Data Centric Systems
Soggetto topico Computational intelligence
Cloud computing
Big data
ISBN 0-12-813327-9
0-12-813314-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910583315803321
London, United Kingdom : , : Academic Press, an imprint of Elsevier, , [2018]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Data Quality and Trust in Big Data : 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Dubai, UAE, November 12–15, 2018, Revised Selected Papers / / edited by Hakim Hacid, Quan Z. Sheng, Tetsuya Yoshida, Azadeh Sarkheyli, Rui Zhou
Data Quality and Trust in Big Data : 5th International Workshop, QUAT 2018, Held in Conjunction with WISE 2018, Dubai, UAE, November 12–15, 2018, Revised Selected Papers / / edited by Hakim Hacid, Quan Z. Sheng, Tetsuya Yoshida, Azadeh Sarkheyli, Rui Zhou
Edizione [1st ed. 2019.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Descrizione fisica 1 online resource (IX, 137 p. 45 illus., 19 illus. in color.)
Disciplina 005.7
Collana Information Systems and Applications, incl. Internet/Web, and HCI
Soggetto topico Application software
Information storage and retrieval systems
Artificial intelligence
Electronic digital computers - Evaluation
Computer and Information Systems Applications
Information Storage and Retrieval
Artificial Intelligence
System Performance and Evaluation
ISBN 9783030191436
3030191435
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto A Novel Data Quality Metric for Minimality -- Automated Schema Quality Measurement in Large-scale Information Systems -- Email Importance Evaluation in Mailing List Discussions -- SETTRUST: Social Exchange Theory Based Context- Aware Trust Prediction in Online Social Networks -- CNR: Cross-Network Recommendation Embedding User’s Personality -- Firefly Algorithm with Proportional Adjustment Strategy -- A Formal Taxonomy of Temporal Data Defects -- Data-intensive Computing Acceleration with Python in Xilinx FPGA -- Delone and McLean IS Success Model for Evaluating Knowledge Sharing.
Record Nr. UNINA-9910337846803321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Intelligent Decision Support Systems for Sustainable Computing : Paradigms and Applications / / edited by Arun Kumar Sangaiah, Ajith Abraham, Patrick Siarry, Michael Sheng
Intelligent Decision Support Systems for Sustainable Computing : Paradigms and Applications / / edited by Arun Kumar Sangaiah, Ajith Abraham, Patrick Siarry, Michael Sheng
Edizione [1st ed. 2017.]
Pubbl/distr/stampa Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Descrizione fisica 1 online resource (XVI, 289 p. 108 illus., 86 illus. in color.)
Disciplina 658.403
Collana Studies in Computational Intelligence
Soggetto topico Computational intelligence
Artificial intelligence
Computational Intelligence
Artificial Intelligence
ISBN 3-319-53153-0
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Intelligent Decision Support Systems for Sustainable Computing -- A Genetic Algorithm based Efficient Load Distribution Strategy for Handling Large Scale Workloads on Sustainable Computing Systems -- Efficiency in Energy Decision Support Systems using Soft Computing Techniques -- Computational Intelligence Based Heuristic Approach for Maximizing Energy Efficiency in Internet of Things -- Distributed Algorithm with Inherent Intelligence for Multi-Cloud Resource Provisioning -- Parameter Optimization methods based on Computational Intelligence Techniques in Context of Sustainable Computing -- The Maximum Power Point tracking using Fuzzy Logic Algorithm for DC Motor based Conveyor System -- Differential Evolution Based Significant Data Region Identification on Large Storage Drives -- A Fuzzy Based Power Switching Selection for Residential Application to beat Peak Time Power Demand -- Energy Saving Using Memorization: A Novel Energy-Efficient and Fault Tolerant Algorithm -- Analyzing Slavic Textual Sentiment using Deep Convolutional Neural Networks -- Intelligent Decision Support System for an Integrated Pest Management in Apple Orchard -- Analysis of Error Propagation in Safety Critical Software Systems: An Approach based on UGF -- A Framework for Analyzing Uncertainty in Data using Computational Intelligence Techniques -- .
Record Nr. UNINA-9910254342303321
Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Machine learning for computer and cyber security : principles, algorithms, and practices / / editors, Brij B. Gupta, Michael Sheng
Machine learning for computer and cyber security : principles, algorithms, and practices / / editors, Brij B. Gupta, Michael Sheng
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2019]
Descrizione fisica 1 online resource (365 pages)
Disciplina 006.3/1
Soggetto topico Computer networks - Security measures - Data processing
Computer security - Data processing
Machine learning
Artificial intelligence
ISBN 0-429-99572-5
0-429-50404-7
0-429-99571-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright Page; Dedication; Foreword; Acknowledgement; Preface; Table of Contents; 1: A Deep Learning-based System for Network Cyber Threat Detection; 2: Machine Learning for Phishing Detection and Mitigation; 3: Next Generation Adaptable Opportunistic Sensing-based Wireless Sensor Networks: A Machine Learning Perspective; 4: A Bio-inspired Approach to Cyber Security; 5: Applications of a Model to Evaluate and Utilize Users' Interactions in Online Social Networks; 6: A Deep-dive on Machine Learning for Cyber Security Use Cases
7: A Prototype Method to Discover Malwares in Android-based Smartphones through System Calls8: Metaheuristic Algorithms-based Feature Selection Approach for Intrusion Detection; 9: A Taxonomy of Bitcoin Security Issues and Defense Mechanisms; 10: Early Detection and Prediction of Lung Cancer using Machine-learning Algorithms Applied on a Secure Healthcare Data-system Architecture; 11: Preventing Black Hole Attack in AODV Routing Protocol using Dynamic Trust Handshake-based Malicious Behavior Detection
12: Detecting Controller Interlock-based Tax Evasion Groups in a Corporate Governance Network13: Defending Web Applications against JavaScript Worms on Core Network of Cloud Platforms; 14: Importance of Providing Incentives and Economic Solutions in IT Security; 15: Teaching Johnny to Thwart Phishing Attacks: Incorporating the Role of Self-efficacy into a Game Application; Index
Record Nr. UNINA-9910793310903321
Boca Raton : , : CRC Press, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Machine learning for computer and cyber security : principles, algorithms, and practices / / editors, Brij B. Gupta, Michael Sheng
Machine learning for computer and cyber security : principles, algorithms, and practices / / editors, Brij B. Gupta, Michael Sheng
Pubbl/distr/stampa Boca Raton : , : CRC Press, , [2019]
Descrizione fisica 1 online resource (365 pages)
Disciplina 006.3/1
Soggetto topico Computer networks - Security measures - Data processing
Computer security - Data processing
Machine learning
Artificial intelligence
ISBN 0-429-99572-5
0-429-50404-7
0-429-99571-7
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Title Page; Copyright Page; Dedication; Foreword; Acknowledgement; Preface; Table of Contents; 1: A Deep Learning-based System for Network Cyber Threat Detection; 2: Machine Learning for Phishing Detection and Mitigation; 3: Next Generation Adaptable Opportunistic Sensing-based Wireless Sensor Networks: A Machine Learning Perspective; 4: A Bio-inspired Approach to Cyber Security; 5: Applications of a Model to Evaluate and Utilize Users' Interactions in Online Social Networks; 6: A Deep-dive on Machine Learning for Cyber Security Use Cases
7: A Prototype Method to Discover Malwares in Android-based Smartphones through System Calls8: Metaheuristic Algorithms-based Feature Selection Approach for Intrusion Detection; 9: A Taxonomy of Bitcoin Security Issues and Defense Mechanisms; 10: Early Detection and Prediction of Lung Cancer using Machine-learning Algorithms Applied on a Secure Healthcare Data-system Architecture; 11: Preventing Black Hole Attack in AODV Routing Protocol using Dynamic Trust Handshake-based Malicious Behavior Detection
12: Detecting Controller Interlock-based Tax Evasion Groups in a Corporate Governance Network13: Defending Web Applications against JavaScript Worms on Core Network of Cloud Platforms; 14: Importance of Providing Incentives and Economic Solutions in IT Security; 15: Teaching Johnny to Thwart Phishing Attacks: Incorporating the Role of Self-efficacy into a Game Application; Index
Record Nr. UNINA-9910799931803321
Boca Raton : , : CRC Press, , [2019]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui