dmlc--dgl
Mufei Li
47 行
2.5 KiB
Plaintext
47 行
2.5 KiB
Plaintext
.. _tutorials1-index:
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Graph neural networks and its variants
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====================================
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* **Graph convolutional network (GCN)** `[research paper] <https://arxiv.org/abs/1609.02907>`__ `[tutorial]
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<1_gnn/1_gcn.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/blob/master/examples/pytorch/gcn>`__
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`[MXNet code]
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<https://github.com/dmlc/dgl/tree/master/examples/mxnet/gcn>`__:
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This is the most basic GCN. The tutorial covers the basic uses of DGL APIs.
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* **Graph attention network (GAT)** `[research paper] <https://arxiv.org/abs/1710.10903>`__ `[tutorial]
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<1_gnn/9_gat.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/blob/master/examples/pytorch/gat>`__
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`[MXNet code]
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<https://github.com/dmlc/dgl/tree/master/examples/mxnet/gat>`__:
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GAT extends the GCN functionality by deploying multi-head attention
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among neighborhood of a node. This greatly enhances the capacity and
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expressiveness of the model.
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* **Relational-GCN** `[research paper] <https://arxiv.org/abs/1703.06103>`__ `[tutorial]
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<1_gnn/4_rgcn.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/rgcn>`__
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`[MXNet code]
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<https://github.com/dmlc/dgl/tree/master/examples/mxnet/rgcn>`__:
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Relational-GCN allows multiple edges among two entities of a
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graph. Edges with distinct relationships are encoded differently.
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* **Line graph neural network (LGNN)** `[research paper] <https://openreview.net/pdf?id=H1g0Z3A9Fm>`__ `[tutorial]
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<1_gnn/6_line_graph.html>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/line_graph>`__:
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This network focuses on community detection by inspecting graph structures. It
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uses representations of both the original graph and its line-graph
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companion. In addition to demonstrating how an algorithm can harness multiple
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graphs, this implementation shows how you can judiciously mix simple tensor
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operations and sparse-matrix tensor operations, along with message-passing with
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DGL.
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* **Stochastic steady-state embedding (SSE)** `[research paper] <http://proceedings.mlr.press/v80/dai18a/dai18a.pdf>`__ `[tutorial]
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<1_gnn/8_sse_mx.html>`__ `[MXNet code]
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<https://github.com/dmlc/dgl/blob/master/examples/mxnet/sse>`__:
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SSE is an example to illustrate the co-design of both algorithm and
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system. Sampling to guarantee asymptotic convergence while lowering
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complexity and batching across samples for maximum parallelism. The emphasis
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here is that a giant graph that cannot fit comfortably on one GPU card.
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