Titlebook: Web and Big Data; 7th International Jo Xiangyu Song,Ruyi Feng,Geyong Min Conference proceedings 2024 The Editor(s) (if applicable) and The

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,Distributed Deep Learning for?Big Remote Sensing Data Processing on?Apache Spark: Geological Remoteghts into Earth’s surface’s objects are gained with the help of remote sensing processing methods and techniques and are applied in various applications. Recently, deep-learning-based methods are widely used in remote sensing data processing due to their ability to mine relationships using multiple

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,Graph-Enforced Neural Network for?Attributed Graph Clustering,ttribute vector (i.e., the attributed graph). Recently, methods built on Graph Auto-Encoder (GAE) have achieved state-of-the-art performance on the attributed graph clustering task. The performance gain mainly comes from GAE’s ability to capture knowledge from graph structures and node attributes si

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,Graph-Enforced Neural Network for?Attributed Graph Clustering,ttribute vector (i.e., the attributed graph). Recently, methods built on Graph Auto-Encoder (GAE) have achieved state-of-the-art performance on the attributed graph clustering task. The performance gain mainly comes from GAE’s ability to capture knowledge from graph structures and node attributes si

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,MacGAN: A Moment-Actor-Critic Reinforcement Learning-Based Generative Adversarial Network for?Molecug discovery. However, GANs are typically employed to process continuous data such as images and are unstable in performance for discrete molecular graphs and simplified molecular-input line-entry system (SMILES) strings. Most previous studies use reinforcement learning (RL) methods (e.g., Monte Car

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,Multi-modal Graph Convolutional Network for?Knowledge Graph Entity Alignment,data, such as attributions and images, are widely used to enhance alignment performance. However, most existing techniques for multi-modal knowledge exploitation separately pre-train uni-modal features and heuristically merge these features, failing to adequately consider the interplay between diffe

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,Multi-modal Graph Convolutional Network for?Knowledge Graph Entity Alignment,data, such as attributions and images, are widely used to enhance alignment performance. However, most existing techniques for multi-modal knowledge exploitation separately pre-train uni-modal features and heuristically merge these features, failing to adequately consider the interplay between diffe