GitLab

* Update graph

* Fix for dgl.graph

* from_scipy

* Replace canonical_etypes with relations

* from_networkx

* Update for hetero_from_relations

* Roll back the change of canonical_etypes to relations

* heterograph

* bipartite

* Update doc

* Fix lint

* Fix lint

* Fix test cases

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Fix test

* Fix

* Update

* Use DGLError

* Update

* Update

* Update

* Update

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Fix

* Update

* Fix

* Fix

* Fix

* Update

* Fix

* Update

* Fix

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Fix

* Fix

* Update

* Update

* Update

* Update

* Update

* Update

* rewrite sanity checks

* delete unnecessary checks

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Update

* Fix

* Update

* Update

* Update

* Fix

* Fix

* Fix

* Update

* Fix

* Update

* Fix

* Fix

* Update

* Fix

* Update

* Fix
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>
Co-authored-by: Minjie Wang <wmjlyjemaine@gmail.com>
Co-authored-by: Quan Gan <coin2028@hotmail.com>

* add chebconv

* fix

* lint

* fix lint

* docs

* [Doc] User guide section 5 and 6

* oops

* fix

* PPIDataset

* Revert "PPIDataset"

This reverts commit 264bd0c960cfa698a7bb946dad132bf52c2d0c8a.

* data pipeline user guide

* remove chapter numbers

* Update data.rst

* image in dataset userguide

* update links using ref

* modify the link of save_graphs and load_graphs in dataset user guide

* PPIDataset

* Revert "PPIDataset"

This reverts commit 264bd0c960cfa698a7bb946dad132bf52c2d0c8a.

* data pipeline user guide

* remove chapter numbers

* Update data.rst

* image in dataset userguide

* update links using ref

* update knn graph docs

* more docs

* [Doc] transform module docstrings

* remove copy_ndata and copy_edata

* fix

* lint

* fix

* fix

* fix

* clean up docstrings

* fix docstring

* dtype specifications

* addresses comments

* fix
Co-authored-by: Mufei Li <mufeili1996@gmail.com>
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>

* go through gcn, relgcn

* fix tagconv formula

* fix doc in sageconv

* fix sgconv doc

* replace hat with tilde

* more comments on gmmconv

* fix agnnconv chebconv doc

* modify nnconv doc

* remove &

* add nn conv examples

* Rebase master

* More merge conflicts

* check homo

* add back self loop for some convs, check homo in tranform

* add example for denseconv

* add example and doc for dotgat and cfconv

* check in-degree for graphconv

* add language fix

* gconv address all comments

* another round of change based on api template

* change agnn

* go through agnn, appnp, atomic, cf, cheb, dense, gat, sage modules

* finish pytorch part of nn conv

* mxnet graphconv done

* tensorflow graphconv works

* add new modules into doc

* add comments to not split code

* refine doc

* resr

* more comments

* more fix

* finish conv and dense conv part api

* pylint fix

* fix pylink

* fix pylint

* more fix

* fix

* fix test fail because zere in degree

* fix test fail

* sage is not update for mxnet tf
Co-authored-by: Ubuntu <ubuntu@ip-172-31-0-81.us-east-2.compute.internal>

* clean commit

* oops forgot the most important files

* use einsum

* copy feature from frontier to block

* Revert "copy feature from frontier to block"

This reverts commit 5224ec963eb6a3ef1b6ab74d8ecbd44e4e42f285.

* temp fix

* unit test

* fix

* revert jtnn

* lint

* fix win64

* docstring fixes and doc indexing

* revert einsum in sparse bidecoder

* fix some examples

* lint

* fix due to some tediousness in remove_edges

* addresses comments

* fix

* more jtnn fixes

* fix

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* PPIDataset

* Revert "PPIDataset"

This reverts commit 264bd0c960cfa698a7bb946dad132bf52c2d0c8a.

* update doc

* Update data.rst
Co-authored-by: Jinjing Zhou <VoVAllen@users.noreply.github.com>
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>

* PPIDataset

* Revert "PPIDataset"

This reverts commit 264bd0c960cfa698a7bb946dad132bf52c2d0c8a.

* update data rst

* update data doc and docstring

* API doc rst for dataset

* docstring

* update api doc

* add url format

* update docstring

* update citation graph

* update knowledge graph

* update gc datasets

* fix index

* Rst fix (#3)

* Fix syntax

* syntax

* update docstring

* update doc (#4)

* final update

* fix rdflib

* fix rdf
Co-authored-by: HuXiangkun <huxk_hit@qq.com>
Co-authored-by: Ubuntu <ubuntu@ip-172-31-51-214.ec2.internal>
Co-authored-by: xiang song(charlie.song) <classicxsong@gmail.com>

* upd

* fix typo

* upd

* upd

* fix typo

* upd

* upd

* upd

* upd

* upd

* upd

* upd

* relax

* relax too

* upd

* 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

* 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>

* 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

* 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>

* 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

* 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

* 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

* 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

* Start implementing masked-mm kernel.

Add base control flow code.

* Add masked dot declare

* Update func/variable name

* Skeleton compile OK

* Update Implement. Unify BinaryDot with BinaryReduce

* New Impl of x_dot_x, reuse binary reduce template

* Compile OK.

TODO:
1. make sure x_add_x, x_sub_x, x_mul_x, x_div_x work
2. let x_dot_x work
3. make sure backward of x_add_x, x_sub_x, x_mul_x, x_div_x work
4. let x_dot_x backward work

* Fix code style

* Now we can pass the tests/compute/test_kernel.py for add/sub/mul/div forward and backward

* Fix mxnet test code

* Add u_dot_v, u_dot_e, v_dot_e unitest.

* Update doc

* Now also support v_dot_u, e_dot_u, e_dot_v

* Add unroll for some loop

* Add some Opt for cuda backward of dot builtin.

Backward is still slow for dot

* Apply UnravelRavel opt for broadcast backward

* update docstring