.. _sphx_glr_tutorials_models_1_gnn: .. _tutorials1-index: Graph neural networks and its variants -------------------------------------------- * **Graph convolutional network (GCN)** `[research paper] `__ `[tutorial] <1_gnn/1_gcn.html>`__ `[Pytorch code] `__ `[MXNet code] `__: * **Graph attention network (GAT)** `[research paper] `__ `[tutorial] <1_gnn/9_gat.html>`__ `[Pytorch code] `__ `[MXNet code] `__: GAT extends the GCN functionality by deploying multi-head attention among neighborhood of a node. This greatly enhances the capacity and expressiveness of the model. * **Relational-GCN** `[research paper] `__ `[tutorial] <1_gnn/4_rgcn.html>`__ `[Pytorch code] `__ `[MXNet code] `__: Relational-GCN allows multiple edges among two entities of a graph. Edges with distinct relationships are encoded differently. * **Line graph neural network (LGNN)** `[research paper] `__ `[tutorial] <1_gnn/6_line_graph.html>`__ `[Pytorch code] `__: This network focuses on community detection by inspecting graph structures. It uses representations of both the original graph and its line-graph companion. In addition to demonstrating how an algorithm can harness multiple graphs, this implementation shows how you can judiciously mix simple tensor operations and sparse-matrix tensor operations, along with message-passing with DGL. .. raw:: html
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Graph Convolutional Network
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Relational Graph Convolutional Network
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Line Graph Neural Network
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Understand Graph Attention Network
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.. toctree:: :hidden: /tutorials/models/1_gnn/1_gcn /tutorials/models/1_gnn/4_rgcn /tutorials/models/1_gnn/6_line_graph /tutorials/models/1_gnn/9_gat