GitLab

* neighbor sampler data loader first commit

* more commit

* nodedataloader

* fix

* update RGCN example

* update OGB

* fixes

* fix minibatch RGCN crashing with self loop

* reverting gatconv test code

* fix

* change to new solution that doesn't require tf dataloader

* fix

* lint

* fix

* fixes

* change doc

* fix docstring

* docstring fixes

* return seeds and input nodes from data loader

* fixes

* fix test

* fix windows build problem

* add pytorch wrapper

* fixes

* add pytorch wrapper

* add unit test

* add -1 support to sample_neighbors & fix docstrings

* docstring fix

* lint

* add minibatch rgcn evaluations
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
Co-authored-by: Tong He <hetong007@gmail.com>

* Move LifeSci

* Remove doc

* Use new version of Cora

* Fix import

* add cuda 10.2

* type

* fix docs of to_block

* fix

* oops

* more fixes

* upd

* simplify

* further simplify

* lint

* doc

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* lint

* rename format

* upd

* lint

* upd

* upd

* upd

* udp

* debug

* upd

* upd

* upd

* upd

* upd

* 无可厚非吧

* 中三边肥

* 你一定要喊吗

* lint

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* Update unit_graph.h
Co-authored-by: Zihao Ye <yzh119@192.168.0.110>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Quan (Andy) Gan <coin2028@hotmail.com>

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

* Update

* Update

* Update

* TF backend fix and new logic to choose backend

* fix

* fix

* fix

* fix

* fix backend

* fix

* dlpack alignment

* add flag

* flag

* lint

* lint

* remove unused

* several fixes
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

Co-authored-by: Tong He <hetong007@gmail.com>

Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>

Co-authored-by: Ubuntu <ubuntu@ip-172-31-60-78.ec2.internal>

* upd

* upd

* upd

* lint

* fix

* fix test

* fix

* fix

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd tutorial

* upd

* upd

* fix kg

* upd doc organization

* refresh test

* upd

* refactor doc

* fix lint
Co-authored-by: Minjie Wang <minjie.wang@nyu.edu>

* reorg tutorials and api docs

* fix

* add env var

* Trigger CI

* simplification

* add doc

* several nn example

* appnp

* fix lint

* lint

* add dgi

* fix

* fix

* fix

* fff

* docs

* 111

* fix

* change init

* change result

* tiaocan+1

* fix

* fix lint

* fix

* fix

* upd

* upd

* upd

* upd

* lint

* upd

* upd

* upd

* upd
Co-authored-by: VoVAllen <VoVAllen@users.noreply.github.com>

* change download link to cdn

* Update env_var.rst

* update work with different backend section

* fix some warnings

* Update backend.rst

* Update index.rst
Co-authored-by: VoVAllen <VoVAllen@users.noreply.github.com>

* Add several splitting methods

* Update

* Update

* Update

* Update

* Update

* Fix

* Update

* Update

* Update

* Update

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Finally

* CI

* Update contribute.rst

* Update contribute.rst

* Update contribute.rst

* Update contribute.rst

* Edit for grammar and style

Improve the flow and readability

* Update docs/source/features/nn.rst

Better now? NN Modules as a title is vague

* grammatical updates

Edit pass for readability. 
Can you clarify: "are of different, but shapes that can be broadcast." Are they of different shapes, but both can be broadcast?

* Update docs/source/features/builtin.rst

Okay now? Check this for logic.

* Grammar and style edit pass

In the opening, it would be great to provide some rationale for why you recommend conda or pip.

* Update docs/source/install/index.rst
Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update docs/source/install/index.rst
Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update docs/source/install/index.rst
Co-Authored-By: Aaron Markham <markhama@amazon.com>

* Update

* Update

* Update

* Fix

* CI style fix

* CI fix style

* Fix

* Try CI

* Fix test

* Update

* Update

* Update

* Update

* upd

* damn it

* fuck

* fuck pylint

* fudge

* remove some comments about MXNet

* upd

* upd

* damn it

* damn it

* fuck

* fuck

* upd

* upd

* pylint bastard

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* Update

* Fix style

* Update

* Update

* Fix

* Update

* Update

* Refactor

* Add note

* Update

* CI

* minor spelling updates

* Update docs/source/features/builtin.rst

* heterograph tutorial skeleton

* [WIP][Tutorial] Heterogeneous graph tutorial

* fix

* update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* style fixes & undefined name fix

* transpose=False in test

* upd

* fig edgebatch edges

* add test

* trigger

* Update README.md for pytorch PinSage example.

Add noting that the PinSage model example under
example/pytorch/recommendation only work with Python 3.6+
as its dataset loader depends on stanfordnlp package
which work only with Python 3.6+.

* Provid a frame agnostic API to test nn modules on both CPU and CUDA side.

1. make dgl.nn.xxx frame agnostic
2. make test.backend include dgl.nn modules
3. modify test_edge_softmax of test/mxnet/test_nn.py and
    test/pytorch/test_nn.py work on both CPU and GPU

* Fix style

* Delete unused code

* Make agnostic test only related to tests/backend

1. clear all agnostic related code in dgl.nn
2. make test_graph_conv agnostic to cpu/gpu

* Fix code style

* fix

* doc

* Make all test code under tests.mxnet/pytorch.test_nn.py
work on both CPU and GPU.

* Fix syntex

* Remove rand

* Add TAGCN nn.module and example

* Now tagcn can run on CPU.

* Add unitest for TGConv

* Fix style

* For pubmed dataset, using --lr=0.005 can achieve better acc

* Fix style

* Fix some descriptions

* trigger

* Fix doc

* Add nn.TGConv and example

* Fix bug

* Update data in mxnet.tagcn test acc.

* Fix some comments and code

* delete useless code

* Fix namming

* Fix bug

* Fix bug

* Add test for mxnet TAGCov

* Add test code for mxnet TAGCov

* Update some docs

* Fix some code

* Update docs dgl.nn.mxnet

* Update weight init

* Fix

* init version.

* change default value of regularization.

* avoid specifying adversarial_temperature

* use default eval_interval.

* remove original model.

* remove optimizer.

* set default value of num_proc

* set default value of log_interval.

* don't need to set neg_sample_size_valid.

* remove unused code.

* use uni_weight by default.

* unify model.

* rename model.

* remove unnecessary data sampler.

* remove the code for checkpoint.

* fix eval.

* raise exception in invalid arguments.

* remove RowAdagrad.

* remove unsupported score function for now.

* Fix bugs of kg
Update README

* Update Readme for mxnet distmult

* Update README.md

* Update README.md

* revert changes on dmlc

* add tests.

* update CI.

* add tests script.

* reorder tests in CI.

* measure performance.

* add results on wn18

* remove some code.

* rename the training script.

* new results on TransE.

* remove --train.

* add format.

* fix.

* use EdgeSubgraph.

* create PBGNegEdgeSubgraph to simplify the code.

* fix test

* fix CI.

* run nose for unit tests.

* remove unused code in dataset.

* change argument to save embeddings.

* test training and eval scripts in CI.

* check Pytorch version.

* fix a minor problem in config.

* fix a minor bug.

* fix readme.

* Update README.md

* Update README.md

* Update README.md

* convert np.ndarray to backend tensor

* add datasets

* add qm7

* add dataset

* add dataset

* fix

* change ppi

* tu dataset

* add datasets

* fix

* fix

* fix

* fix

* add docstring

* docs

* doc

* add transform

* lint

* lint

* fix

* fixmx

* fix

* add test

* fix typo

* fix default num_classes

* change to non-inplace operation

* fix lint

* fix

* fix

* fix lint

* fixlint

* add transform

* lint

* lint

* fix

* fixmx

* fix

* add test

* fix typo

* fix default num_classes

* change to non-inplace operation

* fix lint

* fix