Machine Learning and Knowledge Discovery in Databases : Research Track - Info and Reading Options

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Intro -- Preface -- Organization -- Invited Talks Abstracts -- Neural Wave Representations -- Physics-Inspired Graph Neural Networks -- Mapping Generative AI -- Contents - Part III -- Graph Neural Networks -- Learning to Augment Graph Structure for both Homophily and Heterophily Graphs -- 1 Introduction -- 2 Related Work -- 2.1 Graph Neural Networks -- 2.2 Graph Structure Augmentation -- 2.3 Variational Inference for GNNs -- 3 Methodology -- 3.1 Problem Statement -- 3.2 Augmentation from a Probabilistic Generation Perspective -- 3.3 Iterative Variational Inference -- 3.4 Parameterized Augmentation Distribution -- 3.5 GNN Classifier Module for Node Classification -- 3.6 Complexity Analysis -- 4 Experiments -- 4.1 Experimental Setups -- 4.2 Classification on Real-World Datasets (Q1) -- 4.3 Homophily Ratios and GNN Architectures (Q2) -- 4.4 Ablation Study (Q3) -- 4.5 Augmentation Strategy Learning (Q4) -- 4.6 Parameter Sensitivity Analysis (Q5) -- 5 Conclusion -- References -- Learning Representations for Bipartite Graphs Using Multi-task Self-supervised Learning -- 1 Introduction -- 2 Background Work -- 2.1 Bipartite Graph Representation Learning -- 2.2 Self Supervised Learning (SSL) for GNNs -- 2.3 Multi-task Self Supervised Learning and Optimization -- 3 Proposed Algorithm -- 3.1 Notation -- 3.2 Bipartite Graph Encoder -- 3.3 Multi Task Self Supervised Learning -- 3.4 DST++: Dropped Schedule Task MTL with Task Affinity -- 4 Experiments -- 4.1 Datasets -- 4.2 Downstream Tasks and Evaluation Metrics -- 4.3 Evaluation Protocol -- 4.4 Baselines -- 5 Results and Analysis -- 5.1 Comparison with Unsupervised Baselines -- 5.2 Ablation Study -- 6 Conclusion -- References -- ChiENN: Embracing Molecular Chirality with Graph Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Order-Sensitive Message-Passing Scheme

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