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