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