LEADER 12847nam 22008895 450 001 9910728394703321 005 20230615193851.0 010 $a3-031-33377-2 024 7 $a10.1007/978-3-031-33377-4 035 $a(MiAaPQ)EBC30553115 035 $a(Au-PeEL)EBL30553115 035 $a(OCoLC)1380746696 035 $a(DE-He213)978-3-031-33377-4 035 $a(BIP)091205957 035 $a(PPN)270612378 035 $a(CKB)26784642300041 035 $a(EXLCZ)9926784642300041 100 $a20230527d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aAdvances in Knowledge Discovery and Data Mining $e27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, Osaka, Japan, May 25?28, 2023, Proceedings, Part II /$fedited by Hisashi Kashima, Tsuyoshi Ide, Wen-Chih Peng 205 $a1st ed. 2023. 210 1$aCham :$cSpringer Nature Switzerland :$cImprint: Springer,$d2023. 215 $a1 online resource (562 pages) 225 1 $aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13936 311 08$aPrint version: Kashima, Hisashi Advances in Knowledge Discovery and Data Mining Cham : Springer,c2023 9783031333767 320 $aIncludes bibliographical references and index. 327 $aIntro -- General Chairs' Preface -- PC Chairs' Preface -- Organization -- Contents - Part II -- Graphs and Networks -- Improving Knowledge Graph Entity Alignment with Graph Augmentation -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 4 Methodology -- 4.1 Entity-Relation Encoder -- 4.2 Model Training with Graph Augmentation -- 4.3 Alignment Inference -- 5 Experimental Setup -- 5.1 Experimental Setup -- 5.2 Experimental Results -- 6 Discussion and Conclusion -- References -- MixER: MLP-Mixer Knowledge Graph Embedding for Capturing Rich Entity-Relation Interactions in Link Prediction -- 1 Introduction -- 2 Related Work -- 2.1 Translation-Based Approaches -- 2.2 Matrix Factorization-Based Approaches -- 2.3 Neural Network-Based Approaches -- 3 Methodology -- 3.1 Problem Formulation and Notations -- 3.2 Overall Architecture Design -- 3.3 Model Architecture -- 4 Experiments -- 4.1 Datasets -- 4.2 Evaluation Protocol and Metric -- 4.3 Hyperparameters and Baselines -- 4.4 Results and Discussion -- 4.5 Analysis -- 5 Conclusion and Future Work -- References -- GTEA: Inductive Representation Learning on Temporal Interaction Graphs via Temporal Edge Aggregation -- 1 Introduction -- 2 Related Works -- 2.1 Temporal Dynamics Modeling on Graph-Structured Data -- 2.2 Representation Learning on Graphs with Edge Features -- 3 Proposed Methods -- 3.1 Problem Formulation -- 3.2 Overview of GTEA -- 3.3 Learning Edge Embeddings for Interaction Sequences -- 3.4 Representation Learning with Temporal Edge Aggregation -- 3.5 Model Training for Different Graph-Related Tasks -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Experimental Results of Overall Performance -- 4.3 Experiments Analyses -- 5 Conclusions -- References -- You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network -- 1 Introduction. 327 $a2 Related Works -- 3 Problem Formulation -- 4 Methodology -- 4.1 Global Topology Structure Extraction -- 4.2 Graph Structure Memory Augmented Representation Learning -- 4.3 Objective Function of GSM-GNN -- 5 Experiments -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Performance on Node Classification -- 5.4 Flexibility of GSM-GNN for Various GNNs -- 5.5 Ablation Study -- 6 Conclusion -- References -- UPGAT: Uncertainty-Aware Pseudo-neighbor Augmented Knowledge Graph Attention Network -- 1 Introduction -- 2 Preliminaries -- 2.1 Problem Statement -- 2.2 Motivations and Challenges -- 2.3 Related Work -- 3 Approach -- 3.1 Overview -- 3.2 1-Hop Attention Module with Attention Baseline Mechanism -- 3.3 Confidence Score Prediction and Training Objective -- 3.4 Pseudo-neighbor Augmented Graph Attention Network -- 4 Experiment -- 4.1 Settings -- 4.2 Results and Analysis -- 4.3 Ablation Study -- 4.4 Deterministic Settings -- 5 Conclusion and Future Work -- References -- Mining Frequent Sequential Subgraph Evolutions in Dynamic Attributed Graphs -- 1 Introduction -- 2 Related Work -- 3 Notations -- 3.1 Dynamic Attributed Graph -- 3.2 A New Pattern Domain -- 3.3 Interesting Measures and Constraints -- 4 Mining Frequent Sequential Subgraph Evolutions -- 4.1 Extraction of Subgraph Candidates -- 4.2 Generation of Size-1 Patterns by Graph Addition -- 4.3 Extension of Patterns -- 5 Experiments -- 6 Conclusion -- References -- CondTraj-GAN: Conditional Sequential GAN for Generating Synthetic Vehicle Trajectories -- 1 Introduction -- 2 Problem Definition -- 3 The CondTraj-GAN Framework -- 3.1 Training -- 3.2 Trajectory Inference -- 4 Evaluation Setup -- 4.1 Dataset -- 4.2 Model Setups -- 4.3 Evaluation Metrics -- 4.4 Baselines -- 5 Evaluation -- 5.1 Trajectory Generation Performance -- 5.2 Ablation Study -- 6 Related Work -- 7 Conclusion and Future Work -- References. 327 $aA Graph Contrastive Learning Framework with Adaptive Augmentation and Encoding for Unaligned Views -- 1 Introduction -- 2 Related Work -- 2.1 Graph Contrastive Learning -- 2.2 Adversarial Training -- 3 Method -- 3.1 Preliminaries -- 3.2 Adaptive Augmentation -- 3.3 Encoding Methods for Homophilic and Heterophilic Graphs -- 3.4 G-EMD-based Contrastive Loss -- 3.5 Adversarial Training on GCAUV -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Performance on Node Classification -- 4.3 Ablation Studies -- 5 Conclusion -- References -- MPool: Motif-Based Graph Pooling -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Preliminaries and Problem Formulation -- 3.2 Motif Based Graph Pooling Models -- 3.3 Readout Function and Output Layer -- 4 Experiment -- 4.1 Overall Evaluation -- 5 Conclusion -- References -- Anti-Money Laundering in Cryptocurrency via Multi-Relational Graph Neural Network -- 1 Introduction -- 2 Methodology -- 2.1 Graph Construction -- 2.2 Representation Embedding -- 2.3 Inter-relation Aggregation -- 2.4 Adaptive Neighbor Sampler -- 3 Experiment -- 3.1 Experimental Setup -- 3.2 Demystifying Mixing Behavior -- 3.3 Performance Comparison -- 3.4 Ablation Study -- 3.5 Adaptive Sampler Analysis -- 4 Conclusion -- References -- Interpretability and Explainability -- CeFlow: A Robust and Efficient Counterfactual Explanation Framework for Tabular Data Using Normalizing Flows -- 1 Introduction -- 2 Related Works -- 3 Preliminaries -- 3.1 Counterfactual Explanation -- 3.2 Normalizing Flow -- 4 Methodology -- 4.1 General Architecture of CeFlow -- 4.2 Normalizing Flows for Categorical Features -- 4.3 Conditional Flow Gaussian Mixture Model for Tabular Data -- 4.4 Counterfactual Generation Step -- 5 Experiments -- 6 Conclusion -- References. 327 $aFeedback Effect in User Interaction with Intelligent Assistants: Delayed Engagement, Adaption and Drop-out -- 1 Introduction -- 2 Related Work -- 3 Data Collection -- 3.1 Study 1: Pre-event Control Period -- 3.2 Study 2: Post-event New User Period -- 4 Feedback Effect on Engagement -- 4.1 Covariates and Outcome Variables -- 4.2 Observational Causal Methods -- 4.3 Time to Next Engagement -- 4.4 Number of Active Days -- 5 Language Convergence in New User Cohort -- 5.1 New and Existing User Cohort Definition -- 5.2 New User's Self-Selection: Drop-out or Adaption -- 6 Discussions -- References -- Toward Interpretable Machine Learning: Constructing Polynomial Models Based on Feature Interaction Trees -- 1 Introduction -- 2 Related Work -- 2.1 SHAP and Pair-Wise Interaction Values -- 2.2 Polynomial Model and EBM -- 3 Methodology -- 3.1 Black-box Model Creation -- 3.2 Global SHAP Interaction Value Score Calculation -- 3.3 Tree-building Process -- 4 Experiments -- 4.1 Model Performance -- 4.2 Evaluating Interpretability -- 4.3 Usability Study -- 5 Conclusion -- References -- Kernel Methods -- BioSequence2Vec: Efficient Embedding Generation for Biological Sequences -- 1 Introduction -- 2 Related Work -- 3 Proposed Approach -- 3.1 BioSequence2Vec Representation -- 4 Experimental Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References -- Matrices and Tensors -- Relations Between Adjacency and Modularity Graph Partitioning -- 1 Introduction -- 2 Preliminaries -- 3 Dominant Eigenvectors of Modularity and Adjacency Matrices -- 4 Normalized Adjacency and Modularity Clustering -- 5 Experiments -- 5.1 Synthetic Data Sets -- 5.2 PenDigit Data Sets from MNIST Database -- 6 Conclusion -- References -- Model Selection and Evaluation -- Bayesian Optimization over Mixed Type Inputs with Encoding Methods -- 1 Introduction -- 2 Related Work. 327 $a2.1 BO for Categorical and Continuous Inputs -- 2.2 Encoding Methods -- 3 Background -- 3.1 Problem Statement -- 3.2 Bayesian Optimization -- 4 The Proposed Framework -- 4.1 Target Mean Encoding BO -- 4.2 Aggregate Ordinal Encoding BO -- 5 Experiments -- 5.1 Baseline Method and Evaluation Measures -- 5.2 Performance and Computation Time -- 6 Conclusion -- References -- Online and Streaming Algorithms -- Using Flexible Memories to Reduce Catastrophic Forgetting -- 1 Introduction -- 2 Related Work -- 3 The Continual Learning Problem -- 4 The Stability Wrapper (SW) for Replay Buffer Replacements -- 5 Experimental Results -- 6 Conclusion -- References -- Fair Healthcare Rationing to Maximize Dynamic Utilities -- 1 Introduction -- 1.1 Our Models -- 1.2 Our Contributions -- 2 Algorithms for Model 1 -- 2.1 Online Algorithm for Model 1 -- 2.2 Charging Scheme -- 2.3 Tight Example for the Online Algorithm -- 3 Online Algorithm for Model 2 -- 3.1 Outline of the Charging Scheme -- 4 Strategy-Proofness of the Online Algorithm -- 5 Experimental Evaluation -- 5.1 Methodology -- 5.2 Datasets -- 5.3 Results and Discussions -- 6 Conclusion -- References -- A Multi-player MAB Approach for Distributed Selection Problems -- 1 Introduction -- 2 Related Work -- 3 Platform Model and Problem Formulation -- 4 The Offline Optimization Problem -- 5 Online Learning Algorithm -- 6 Experiment -- 7 Conclusion -- References -- A Thompson Sampling Approach to Unifying Causal Inference and Bandit Learning -- 1 Introduction -- 2 Model -- 2.1 The Bandit Learning Model -- 2.2 The Data Model -- 2.3 Problem Formulation -- 3 Limitations of Naively Applying Thompson Sampling -- 3.1 VirTS: Naively Applying Thompson Sampling -- 3.2 Limitations of VirTS -- 4 VirTS-DF: Improving VirTS via Offline Data Filtering -- 5 Experiments on Real-world Data -- 5.1 Experimental Settings. 327 $a5.2 Experiment Results. 330 $aThe 4-volume set LNAI 13935 - 13938 constitutes the proceedings of the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining, PAKDD 2023, which took place in Osaka, Japan during May 25?28, 2023. The 143 papers presented in these proceedings were carefully reviewed and selected from 813 submissions. They deal with new ideas, original research results, and practical development experiences from all KDD related areas, including data mining, data warehousing, machine learning, artificial intelligence, databases, statistics, knowledge engineering, big data technologies, and foundations. 410 0$aLecture Notes in Artificial Intelligence,$x2945-9141 ;$v13936 606 $aArtificial intelligence 606 $aAlgorithms 606 $aEducation?Data processing 606 $aComputer science?Mathematics 606 $aComputer vision 606 $aComputer engineering 606 $aComputer networks 606 $aArtificial Intelligence 606 $aDesign and Analysis of Algorithms 606 $aComputers and Education 606 $aMathematics of Computing 606 $aComputer Vision 606 $aComputer Engineering and Networks 610 $aMathematics 615 0$aArtificial intelligence. 615 0$aAlgorithms. 615 0$aEducation?Data processing. 615 0$aComputer science?Mathematics. 615 0$aComputer vision. 615 0$aComputer engineering. 615 0$aComputer networks. 615 14$aArtificial Intelligence. 615 24$aDesign and Analysis of Algorithms. 615 24$aComputers and Education. 615 24$aMathematics of Computing. 615 24$aComputer Vision. 615 24$aComputer Engineering and Networks. 676 $a006.312 676 $a006.312 702 $aKashima$b Hisashi$f1975- 702 $aIde$b Tsuyoshi 702 $aPeng$b Wen-Chih$f1973- 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910728394703321 996 $aAdvances in Knowledge Discovery and Data Mining$9772012 997 $aUNINA