* Add SH tutorials
* setup sphinx-gallery; work on graph tutorial
* draft dglgraph tutorial
* update readme to include document url
* rm obsolete file
* Draft the message passing tutorial
* Capsule code (#102)
* add capsule example
* clean code
* better naming
* better naming
* [GCN]tutorial scaffold
* fix capsule example code
* remove previous capsule example code
* graph struc edit
* modified: 2_graph.py
* update doc of capsule
* update capsule docs
* update capsule docs
* add msg passing prime
* GCN-GAT tutorial Section 1 and 2
* comment for API improvement
* section 3
* Tutorial API change (#115)
* change the API as discusses; toy example
* enable the new set/get syntax
* fixed pytorch utest
* fixed gcn example
* fixed gat example
* fixed mx utests
* fix mx utest
* delete apply edges; add utest for update_edges
* small change on toy example
* fix utest
* fix out in degrees bug
* update pagerank example and add it to CI
* add delitem for dataview
* make edges() return form that is compatible with send/update_edges etc
* fix index bug when the given data is one-int-tensor
* fix doc
1. Update `examples/pytorch/gcn` and `python/dgl/nn/pytorch` based on the latest APIs
2. Add full support for dropout in `examples/pytorch/gcn` and `python/dgl/nn/pytorch`
3. Rename `GCN` class in `python/dgl/nn/pytorch` to be `GraphConvolutionLayer` class
4. Make node field an argument that can be configured by users in GraphConvolutionLayer
Note that adjacency normalization has not been supported yet in the examples.
* support mxnet.
* add mxnet version of GCN.
* rename mxnet.nd as F.
* add mxnet GAT.
* enable GPU for GCN.
* fix MXNet GCN train.
* Use adam to optimize GAT
* support more operators.
* support sparse arrays.
* update mxnet backend.
* support index_copy.
* remove NN.
* update mxnet backend.
* temp check in.
* fix data conversion.
* add test.
* clean up mxnet backend.
* update mxnet examples.
* Revert "remove NN."
This reverts commit d815d9a0ec619f9ce9099c48cd35db9d8e947483.
* temp disable MXNet version of NN.
* WIP: API renaming
* API rewrite and node function refactor
* builtin functions
* builtin functions tested
* fix test
* send and recv spmv test
* WIP: fix examples
* Fix examples using new APIs
* subgraph copy from
* WIP
* cached members
* Change all usage of id tensor to the new Index object; remove set device in DGLGraph;
* subgraph merge API tested
* add dict type reduced msg test
* model code for generative graphs
* batched version for dynamic graph generation using padding
* renaming function train back to forward
* remove old util function for padding DGMG
* override networkx clear to reset state, add dgl.nn
* Dynamic graph without batching
* use relative import path
* load dataset, pad batch
* bug fix
* experimental batch and unbatch
* dgmg batched version
* minor tweak
* move preprocessing padding into data loading
* batch graph test code
* minor
* batched graph class and test cases
* make dgl.nn.gcn a simple layer plus minor fix
* update dgmg model
* test forward using attribute field
* use frame append, minor changes
* moving networkx operations out of forward
* revert some changes
* remove structural immutability check
* fix edge list order problem in cached graph.
* minor fix
* fix bug in edge iter
* SPMV works
* gcn spmv on CPU
* change gcn style
* fix cached graph performance; fixed gcn dataset bug
* reorg dir
* non-batch spmv; partial update problem with shape change
* fix reorder problem; finish gcn-batch impl
* pop API
* GPU context
* add reduce_msg related api to dgl graph
* add reduce_sum, switch backend from numpy to pytorch
* update gat gcn to use reduce msg api
* remove reduce_sum
* add built-in reduce functions
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