Dynamic Networks

Overview of Graph Neural Networks: Dynamic Graphs

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Overview of Graph Neural Networks: Dynamic Graphs

Introduction Graph neural networks (GNNs) have been widely applied to the modeling and representation learning of graph-structured data. However, mainstream research has been limited to handling static network data, while real complex networks often undergo structural and property evolution over time. The team led by Katarzyna at the University of Technology Sydney recently published a … Read more

Dynamic Neural Networks: Key Challenges and Solutions

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Dynamic Neural Networks: Key Challenges and Solutions

Originally from Zhiyuan Community [Column: Key Issues]In recent years, we have witnessed increasingly powerful neural network models, such as AlexNet, VGG, GoogleNet, ResNet, DenseNet, and the recently popular Transformer. The processes used by these neural networks can be summarized as follows: 1) Fixed network architecture, initializing network parameters; 2) Training phase: optimizing network parameters on … Read more