{"text_format":"2","workbook":"xls","n_sheets":1,"sheets":["report2"],"not_rendered":{"charts":0,"images":0,"macros":0,"external_links":1,"defined_names":2,"comments":0,"pivot_tables":0},"unopened":[],"bounds":{"document_bytes":16777216,"sheet_cells":100000,"run_cost":"one","rows_first":2000,"rows_last":500,"rows_between":2000,"halvings":0,"cell_chars":16384,"cell_head_chars":4096},"cells_cut":0,"cells_raw":0,"read":"whole","date_system":"1900","formulas":"cached_only"}

{"sheet":"report2","index":0,"n_rows":52,"n_cols":5,"rows_kept":52,"cut":false,"hidden":false,"merged":[[0,2,0,3],[0,4,0,5],[1,2,1,3],[1,4,1,5],[2,2,2,3],[2,4,2,5],[3,2,3,3],[4,2,4,3],[5,2,5,3],[6,2,6,3],[7,2,7,3],[8,2,8,3],[10,2,10,3],[11,2,11,3],[12,2,12,3],[14,2,14,3],[14,4,14,5],[15,2,15,3],[15,4,15,5],[16,2,16,3],[17,2,17,3],[19,2,19,3],[20,2,20,3],[21,2,21,3],[22,2,22,3],[23,2,23,3],[38,2,38,3],[39,2,39,3],[42,2,42,3]],"flags":["formulas_cached_only"]}
[null,"1.","Record Nr.",null,"UNISA990002907260203316"]
[null,null,"Autore",null,"GIBSON, Johanna"]
[null,null,"Titolo",null,"Community resources : intellectual property, international trade and protection of traditional knowledge / Johanna Gibson"]
[null,null,"Pubbl/distr/stampa",null,"Aldershot : Ashgate, copyr. 2005"]
[null,null,"ISBN",null,"0-7546-4436-7"]
[null,null,"Descrizione fisica",null,"VIII, 387 p. ; 24 cm"]
[null,null,"Collana",null,"Globalization and law"]
[null,null,"Disciplina",null,"346.048"]
[null,null,"Soggetti",null,"Proprieta industriale - Legislazione"]
[{"$nulls":4},"Diritti d'autore"]
[null,null,"Collocazione",null,"XXV.1.M. 842 (IG VIII 16 ING 1187)"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,"2.","Record Nr.",null,"UNINA9910983306803321"]
[null,null,"Titolo",null,"Advanced Data Mining and Applications : 20th International Conference, ADMA 2024, Sydney, NSW, Australia, December 3–5, 2024, Proceedings, Part III / / edited by Quan Z. Sheng, Gill Dobbie, Jing Jiang, Xuyun Zhang, Wei Emma Zhang, Yannis Manolopoulos, Jia Wu, Wathiq Mansoor, Congbo Ma"]
[null,null,"Pubbl/distr/stampa",null,"Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2025"]
[null,null,"ISBN",null,"9789819608218"]
[{"$nulls":4},"981960821X"]
[null,null,"Edizione",null,"[1st ed. 2025.]"]
[null,null,"Descrizione fisica",null,"1 online resource (465 pages)"]
[null,null,"Collana",null,"Lecture Notes in Artificial Intelligence, , 2945-9141 ; ; 15389"]
[null,null,"Disciplina",null,"006.3"]
[null,null,"Soggetti",null,"Data mining"]
[{"$nulls":4},"Artificial intelligence"]
[{"$nulls":4},"Application software"]
[{"$nulls":4},"Computer systems"]
[{"$nulls":4},"Education - Data processing"]
[{"$nulls":4},"Computer vision"]
[{"$nulls":4},"Data Mining and Knowledge Discovery"]
[{"$nulls":4},"Artificial Intelligence"]
[{"$nulls":4},"Computer and Information Systems Applications"]
[{"$nulls":4},"Computer System Implementation"]
[{"$nulls":4},"Computers and Education"]
[{"$nulls":4},"Computer Vision"]
[{"$nulls":4},"Mineria de dades"]
[{"$nulls":4},"Congressos"]
[{"$nulls":4},"Llibres electrònics"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Nota di bibliografia","Includes bibliographical references and index."]
[null,null,"Nota di contenuto",null,"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 "]
[{"$nulls":4},"Threat Model -- 4 Our Scheme -- 4.1 Initialization Phase -- 4.2 Query Processing Phase -- 4.3 Verification Phase -- 5 Security Discussion -- 6 Experiments -- 6.1 Setup -- 6.2 Impact of k and Number of Queries on VO Size -- 6.3 Computational Overhead -- 7 Conclusion -- References -- Depth-Enhanced Contrast Attribute Graph Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Deep Graph Clustering -- 2.2 Graph Data Augmentation -- 3 Method -- 3.1 Notations -- 3.2 Deep Enhancement Module -- 3.3 Contrast Learning Module -- 3.4 Self-optimizing Module -- 3.5 Overall Objective -- 4 Experiments -- 4.1 Baseline Dataset -- 4.2 Baseline -- 4.3 Experimental Setup -- 4.4 Evaluation Metrics -- 4.5 Performance Comparison -- 4.6 Ablation Experiment -- 4.7 Sensitivity Analysis -- 4.8 Visualization -- 4.9 Conclusion -- References -- FCMH: Fast Cluster Multi-hop Model for Graph Fraud Detection -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Network -- 2.2 Graph Fraud Detection -- 3 Methodology -- 3.1 Problem Formulation -- 3.2 Random Cluster Subgraph Division -- 3.3 Multi-hop Neighbor Difference Aggregation -- 3.4 Downsampling and Optimization Objective -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Classification Performance -- 4.3 Time Efficiency -- 4.4 Ablation Study -- 5 Conclusion -- References -- Emotion Graph Augmentation for Detecting Fake News in Online Social Networks -- 1 Introduction -- 2 Related Work -- 2.1 Text Based Methods."]
[{"$nulls":4},"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."]
[{"$nulls":4},"4.1 Basic Algorithm -- 4.2 Improved Online Partition Algorithm -- 4.3 Hybrid Hypergraph Partitioner -- 5 Evaluation -- 5.1 Experimental Setup -- 5.2 Hypergraph Partitioning -- 5.3 Experimental on MinMax++ -- 5.4 Study on Hybrid Strategies -- 6 Conclusions -- References -- Graph Fusion Based Autoencoder for Node Clustering -- 1 Introduction -- 2 Related Work -- 2.1 Deep Clustering -- 2.2 "]
[{"$nulls":4},"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."]
[{"$nulls":4},"3.2 Ranking Contrastive Learning -- 3.3 Fault Detection -- 4 Experiment -- 4.1 Experimental Setup(RQ1) -- 4.2 Performance Comparison -- 4.3 Ablation Study(RQ2) -- 4.4 Parameter Analysis(RQ3) -- 5 Conclusion -- References -- GCS: A Graph-Augmented Semi-supervised Contrastive Learning Approach for Imbalanced Dissolved Gas Analysis in Power Transformers -- 1 Introduction -- 2 Preliminaries -- 2.1 Notations -- 2.2 Imbalance Settings and Problem Definition -- 3 Methodology -- 3.1 Graph Construction -- 3.2 Semi-supervised Contrastive Learning -- 3.3 Graph Augmentation -- 3.4 Classification and Model Optimization -- 4 Experiments -- 4.1 Experiment Settings -- 4.2 Main Comparison Results (RQ1) -- 4.3 Ablation Study (RQ2) -- 4.4 Imbalance Comparison(RQ3) -- 5 Conclusion -- References -- Contrastive Learning Based on Bipartite Graphs for Interpretable Knowledge Tracing -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Problem Definition -- 3.2 Model Overview -- 3.3 Embedding and Knowledge Structure -- 3.4 Bipartite Graph Attention Network -- 3.5 Bipartite Graph Contrastive Learning -- 3.6 Prediction -- 3.7 Model Optimization -- 4 Experimental Results -- 4.1 Datasets -- 4.2 Baselines and Experimental Settings -- 4.3 Implementation Details -- 4.4 Performance Analysis -- 4.5 Interpretability Discussion -- 4.6 Ablation Study -- 4.7 Conclusion -- References -- Graph Data Understanding and Interpretation Enabled by Large Language Models -- 1 Introduction -- 2 Method -- 2.1 Preliminary -- 2.2 Heterogeneous Data Representation Learning -- 2.3 Converter Alignment Tuning -- 2.4 Retrieval Augmented Thoughts -- 3 Experiments -- 3.1 Training Details -- 3.2 Baseline Method -- 3.3 Performance Comparison -- 3.4 Ablation Experiments -- 4 Conclusion -- References -- SDM-GAT: StylisticFP Detection Method Based on Graph Attention Network -- 1 Introduction."]
[{"$nulls":4},"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 -- "]
[{"$nulls":4},"2.1 Anomaly Detection -- 2.2 Network Alignment -- 3 Methodology -- 3.1 Anomaly Detection -- 3.2 Network Alignment -- 3.3 Update Anomaly Subgraph Node Set -- 4 Experiment -- 4.1 Experiment Settings -- 4.2 Results -- 4.3 Case Study -- 5 Conclusion -- References -- Path-Aware Siamese Graph Neural Network for Link Prediction -- 1 Introduction -- 2 Related Work -- 3 Method -- 3.1 Problem Formulation -- 3.2 Model Buildup -- 3.3 Contrastive Learning -- 4 Experiments -- 4.1 Datasets and Task -- 4.2 Baselines -- 4.3 Metrics and Settings -- 4.4 Abalation Study -- 5 Conclusion -- References -- GEM-GNN: Group Enhanced Multi-relation Graph Neural Networks for Fraud Detection -- 1 Introduction -- 2 Related Work -- 3 Proposed Model -- 3.1 Problem Definition -- 3.2 Model Architecture -- 3.3 Neighbor Aggregation Module -- 3.4 Group-Based Aggregation Module -- 3.5 Optimization -- 4 Experiments -- 4.1 Experimental Settings -- 4.2 Performance Comparison -- 4.3 Sensitivity Analysis -- 4.4 Training Process Study -- 5 Application -- 6 Conclusion -- References -- Spatial Data Mining -- ESNet: Perceptive Spatial-Spectral Fusion with Multi-stage Reconstruction for Pansharpening -- 1 Introduction -- 2 The Proposed Model -- 2.1 ESNet -- 2.2 Enhanced Spatial Spectral Attention Module -- 2.3 Multi-scale Reconstruction Module -- 2.4 Loss Function -- 3 Experiment -- 3.1 Datasets and Settings -- 3.2 Accuracy Evaluation."]
[{"$nulls":4},"3.3 Results."]
[null,null,null,"Sommario/riassunto","This six-volume set, LNAI 15387-15392, constitutes the refereed proceedings of the 20th International Conference on Advanced Data Mining and Applications, ADMA 2024, held in Sydney, New South Wales, Australia, during December 3–5, 2024. The 159 full papers presented here were carefully reviewed and selected from 422 submissions. These papers have been organized under the following topical sections across the different volumes: - Part I : Applications; Data mining. Part II : Data mining foundations and algorithms; Federated learning; Knowledge graph. Part III : Graph mining; Spatial data mining. Part IV : Health informatics. Part V : Multi-modal; Natural language processing. Part VI : Recommendation systems; Security and privacy issues. ."]