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Edge Convolutional Networks (EdgeConv)

An exploration of EdgeConv, a model focused on dynamic edge creation and edge-based updates in graph neural networks, with applications in point cloud analysis and molecular interactions.

Graph Autoencoders (GAEs) and Variational Graph Autoencoders (VGAEs)

A comprehensive guide to Graph Autoencoders (GAEs) and Variational Graph Autoencoders (VGAEs) for unsupervised learning tasks, such as link prediction and node clustering.

Introduction to Graph Transformers

A detailed guide to transformer-based architectures like Graph-BERT and Graphormer, adapted to graph data.

Introduction to Dynamic Graph Neural Networks

A detailed guide to modeling and learning from time-evolving graphs using advanced Dynamic GNNs like TGAT, EvolveGCN, and TGN.

Heterogeneous Graph Neural Networks (HGNNs)

An in-depth guide to Heterogeneous Graph Neural Networks (HGNNs), explaining how they handle different node and edge types, and their applications in complex graphs like knowledge graphs and recommendation systems.

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