dmlc--dgl
Da Zheng
19 行
878 B
Plaintext
19 行
878 B
Plaintext
.. _tutorials5-index:
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Training on giant graphs
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=============================
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* **Sampling** `[paper] <https://arxiv.org/abs/1710.10568>`__ `[tutorial]
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<5_giant_graph/1_sampling_mx.html>`__ `[MXNet code]
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<https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__ `[Pytorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
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we can perform neighbor sampling and control-variate sampling to train
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graph convolution networks and its variants on a giant graph.
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* **Scale to giant graphs** `[tutorial] <5_giant_graph/2_giant.html>`__
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`[MXNet code] <https://github.com/dmlc/dgl/tree/master/examples/mxnet/sampling>`__
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`[Pytorch code]
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<https://github.com/dmlc/dgl/tree/master/examples/pytorch/sampling>`__:
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We provide two components (graph store and distributed sampler) to scale to
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graphs with hundreds of millions of nodes.
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