* add examples in traversal.py
* message propagate methods
* use the new message propagation for tree-lstm
* update to the new name
* update propagate API doc
* update doc
* add propagate utest
* 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
* Fix
1. Fix two typos in gcn.py and gcn_spmv.py
2. Update README
* Fix GCN module
1. Update the outdated graph convolution layer class
2. Fix a bug in the code where dropout never works. Modules like dropout/batch norm depend on whether we are in the training stage or inference stage.
* Fix a bug in dropout
1. dropout depends on nn.Module.training
* Update GCN module
* Fix README
* Fix dropout & remove self.msg_field
* Fix
* Align with TF implementation
* Make g an argument for forward
* Remove features from the argument of GraphConv layer
* Support for create nodes/edges after setting representations
* Remove redundant commit
* Delete test_init_repr.py
* Test case for dynamic addition
* Base 'add_rows' upon 'append'
* Move test function
* Fix
* test by assertion
* changed add_rows to adding blank rows only; adding convert_to to backend
* moving test to basics
* oops mxnet
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.
* multigraph support on graph index
* more tests
* multigraph flag, bugfix on clear & copy
* networkx interfaces
* including graph index tests in Jenkins
* node subgraph test
* edge subgraphs
* removing duplicates in pred/succ
* more explicit test and doc
* query source and destination from edge id
* subgraphindex
* renaming has_edge to has_edge_between, apply_edges adding eid
* send_on and send_and_recv_on
* DGLGraph edge subgraph
* merged send_on and send_and_recv_on
* change request
* removing hashmap
* creating multigraph by flag; mingw support
* changes per request
* reverting networkx auto multigraph discovery
* notes on send/send_and_recv on multigraphs
* changing test reducer from sum to max
* added a fixme note in spmv scheduler
* 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.
* Test CPP branch CI (#2)
* Fix batching node-only graphs (#62)
* fixing batching with graphs with no edges
* oops forgot test
* fix readme
* Docker and Jenkins (#1)
* docker ci cpu
* install python packages
* docker ci gpu
* add readme
* use dgl cpu image
* run command in container as root
* use python3
* fix test case
* remove nose from docker file
* docker folder readme
* parallelize cpu and gpu
* top level stages
* comment out python2 related installation
* fix
* remove igraph
* building for cpp
* change building order
* export env in test stage
* withEnv
* run docker container as root
* fix test cases
* fix test cases
* minor
* remove old build
Gan Quan