GitLab

* add spmv tests on mxnet.

* test backward.

* add tests for pull.

* use mxnet backend.

* address comment.

* add comments.

* add a test.

* move file.

* debug

* add degree normalization.

* it can converge now.

* remove hidden_data.

* use readonly graph index.

* use subgraph loader.

* refactor code and remove intermediate data.

* split inference.

* mix cpu and gpu training.

* fix mxnet sparse_matrix constructor.

* convert tensor context.

* set up the prediction model.

* load mxnet csr directly.

* add timing.

* move test_sse.py to sse_batch.py

* fix the tensor version of SSE.

* update README.

addresses issue #156

* minor fix

* jake's change

* pickling support

* resorting to suggested way of pickling

* custom attribute pickling check

* working around a weird pytorch pickling bug

* including partial frame case

* pickling everything now

* fix as requested

* update tree lstm

* tree_lstm (new interface)

* simplify pop

* merge qipeng(root)

* upd tree-lstm & tutorial

* upd model

* new capsule tutorial

* capsule for new API

* fix deprecated API

* New tutorial and example

* investigate gc problem

* add viz code

* new capsule tutorial

* remove ipynb

* move u_hat

* add link

* add requirements.txt

* remove ani.save

* update ci to install requirements

* utf-8

* change seed

* graphviz requirement

* accelerate

* little format

* update some markup

* add neighbor sampler.

* fix sampler.

* handle non-existing nodes differently in MapParentIdToSubgraphId.

* fix sampling tests.

* parallelize sampling.

* add more tests.

* fix a bug in subgraph loader.

* get number of sampled nodes directly.

* load mxnet CSR to DGLGraph directly.

* create graph from mxnet csr array directly.

* use subgraph loader in SSE.

* fix a bug in sampling.

* output framework tensor.

* fix.

* remove unnecessary code.

* fix tests.

* add SSE README.

* move to contrib.

* address comments.

* add tests.

* refactor graph caching

* fix mx test

* fix typo

[DOC][Model] Tree-LSTM example and tutorial

* new capsule tutorial

* capsule for new API

* fix deprecated API

* New tutorial and example

* investigate gc problem

* add viz code

* new capsule tutorial

* remove ipynb

* move u_hat

* add link

* add requirements.txt

* remove ani.save

* update ci to install requirements

* add graphviz

* change ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* update ci

* nx package

* update ci

* update ci

* update ci

* fix

* mx dockerfile by zhengda

* python3.6->3.5

* update ci image

* add tutorial test

* fix ci

* fix ssl problem

* minor change

* small fix on traversal utest

* fix syntax

* add matplotlib in image

* fix

* update ci

* update ci

* bfs with edges

* dfs toy test case

* clean up

* style fix

* bugfix

* updating docs for bfs_edges and readouts

* Fix bug in bfs and topo

* remove legacy codes

* fixing builtin src*edge shape mismatch

* bundled function refactor (?)

* fixing names

* readout prototype

* oops

* more fixes

* removing readout prototype

* sum_on() with SPMV, fixing batching with 0 edges

* readouts with segmented sum

* typo (??????)

* fixes NLTK dependency (#125)

* misc fixes including #126 (pushing again)

* sanity check for mxnet

* fixes NLTK dependency (#125) and #126

* reverting to sum_nodes/edges

* 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

* refactor API doc; add markup link in tutorials

* fix readme

* move gat to the new api.

* fix gcn.

* update sse.

* fix dgl core.

* update sse.

* fix small bugs in dgl core.

* fix mxnet tests.

* retrigger

* address comments and fix more bugs.

* fix

* fix tests.

* tree_lstm (new interface)

* simplify pop

* bfs, dfs and topological traversal

* dfs and test cases

* Conflicts:
	python/dgl/graph.py
	src/graph/graph.cc
	src/graph/graph_op.cc

* documentation

* requested changes

* Conflicts:
	Jenkinsfile
	examples/pytorch/gcn/gcn.py
	examples/pytorch/gcn/gcn_spmv.py
	python/dgl/graph.py
	python/dgl/graph_index.py
	src/graph/graph.cc
	src/graph/graph_op.cc

* Conflicts:
	Jenkinsfile
	python/dgl/graph_index.py

* fix lint errors

* fix lint errors

* fix lint errors

* fix test cases

* requested changes

* traversal interface

* [Bug] fixed a typo that caused syntax error (#120)

* WIP

* bfs nodes generator works

* topological traversal

* WIP: dfs_edges

* dfs edges

* dfs labeled edges

* utest for traversal

* fix lint

* fix utest

* code clean

* changes as requested

[Doc] Basic tutorials & two model tutorials

* backend interface

* small fix

* more comments to the data type dict

* WIP

* convert_to and narrow

* WIP

* pytorch and numpy backend; WIP on mxnet backend

* mxnet backend

* narrow

* Fix all usages

* fix for mx

* fix for mx

* fix mx

* fix mx

* fix mx

* fix mx

* fix mx

* fix mx

* fix mx

* revert jenkins

* add sparse_matrix api

* sparse matrix api

* some fixme

* Fix as requested

* pretty printer

* Conflicts:
	python/dgl/data/sbm.py

* refined line_graph implementation

* fix broken api calls

* small fix to trigger CI

* requested change

* 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

* cherry picking optimization from jtnn

* unbatch by slicing frames

* reduce pack

* oops

* support frame read/write with slices

* reverting to unbatch by splitting; slicing is unfriendly to backward

* replacing lru cache with static object factory

* replacing Scheme object with namedtuple

* remove comment

* forgot the find edges interface

* subclassing namedtuple

* 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. 

* add capsule example

* clean code

* better naming

* better naming

* Clean Codes based on pytorch MNIST example

* Clean codes

* Add README