GitLab

* [Model] MPNN

* [Model] MPNN 🔨 reorganize the mpnn/sch/mgcn model & alchemy dataset

* [Model] MPNN alchemy dataloader refactoring

* [Model] Chem model zoo minor change

* [Model] Chem Model Zoo 🔥 remove old samples

* [Model Zoo] molecule regression minor change

* Fix dataset import

* Fix dataset import

* [Model Zoo] molecule regression test set

* [Model Zoo] molecule prediction MPNN model hyperparameter tuning

* [Model Zoo] molecule prediction ✏ mpnn performance update

* DGMG for molecule generation

* Fix CI check

* Fix for CI

* Trial for CI due to shared memory

* Update

* Better interface for dataset configuration

* Update

* Handle corner cases

* Update README

* Fix

* Fix

* Fix

* Fix

* Fix

* Refactor

* Fix

* Fix

* Fix

* Fix

* Fix

* Fix

* Update

* Fix

* Fix

* Fix

* Fix

* Fix

* Finallly

* 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

* rgcn module

* support id input

* WIP: model codes

* use faster index select

* dropout

* self loop

* WIP: link prediction

* fix lint

* WIP: docs

* docstring

* docstring

* merge two child classes

* mxnet rgcn module

* fix lint

* fix lint

* fix rename bug

* add uniform edge sampler

* fix fn name

* docstring

* fix mxnet rgcn module

* fix mx rgcn

* enable test on cuda

* Add early stop

* add mxnet version

* Poke ci

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+.

* [Model zoo] Model zoo (#765)

* tox21

* fix ci

* fix ci

* fix urls to url

* add doc

* remove binary

* model zoo

* test

* markdown

* fix typo

* fix typo

* fix typo

* raise error

* fix lint

* remove unnecessary

* fix doc

* fix

* fix

* fix

* fix

* fix

* fix

* Update

* CI

* Fix

* Fix

* Fix

* Fix

* Fix

* CI

* rrn model and sudoku

* add README

* refine the code, add doc strings

* add sudoku solver

* [Model] SchNet

* [Model] SchNet model

* [Model] Schnet Model fix device-related bug

* [Model] SchNet fix bugs

* [Model] SchNet fix some bugs

* [Model] Schnet 🎨 code indent format

* [Model] SchNet ✏ fix some typos

* formatting

* formatting

* diffpool original file added

* make diffpool fuse up and running

* minor tweak on tu dataset statistics method

* fix tu

* break

* delete break

* pre_org

* diffpool fuse reorg

* fix random shuffling

* fix bn

* add dgl layers

* early stopping

* add readme

* fix

* add diffpool preprocess script

* tweak tu dataset

* tweak

* tweak

* tweak

* tweak

* tweak

* preprocess dataset

* fix early stopping

* fix

* fix

* fix

* tweak

* readme

* code review

* code review

* dataset code review

* update README

* code review

* tu doc

* update speed numbers in readme

* numbers in examples/pytorch/readme

* all pytorch examples

* scan through mxnet examples

* change reddit data

* tweak numerical range for unittest

* fix ci

* fix ci

* fix

* add seed to workaround

* fix gat code to use latest edge softmax module

* avoid transpose

* update README

* use edge_softmax op

* mxnet edge softmax op

* mxnet gat

* update README

* fix unittest

* fix ci

* fix mxnet nn test; relax criteria for prod reducer

* Update README.md for the demo of distributed sampler

* Update README.MD of demo for distributed sampler

* update

* update

* update demo

* update

* update

* update

* add sender

* update

* remove duplicate cpde

* update

* update

* re-use single-machine code

* update

* use relative path

* update

* update

* update

* add __init__.py

* add __init__.py

* import sys, os

* fix typo

* update

* [Model] Add edge dropout to APPNP

[Model] Add edge dropout to APPNP

* [Model] Refactor the sampling examples (#498)

* reorganize sampling code.

* speedup gcn_ns.

* speed up gcn_cv

* fix graphsage_cv.

* undo the modification.

* accel training.

* update readme.

* [Model] Add edge dropout to APPNP

[Model] Add edge dropout to APPNP

update

* add gin model

* convert dataset.py to data_ont_the_fly way and put it into dgl.data module

* convert dataset.py to data_ont_the_fly way and put it into dgl.data module
python code checked

* modified document and reference TUDataset; checked python part and bypass cpp part due to error

* change tensor to numpy in dataset and transform in collate@Dataloader

* Change minor format issue

Change minor format issue

* moved logging; adjusted tqdm etc

* Update gcn_mp.py

* Update train.py

* Update train.py

* Update train.py

* Update gat_batch.py

* Update gat_batch.py

* Update gcn_mp.py

* [Model] Add APPNP model

* update

* Revert "update"

This reverts commit a8e42d19c8b421fb382305a8f04d7426e78ea53c.

* update

* Update appnp.py

* random walk traces generation

* remove outdated comments

* oops put in the wrong place

* explicit inline

* moving rand_r to util

* pinsage-like model on movielens

* the code runs now

* support cuda

* using readonly graph

* moving random walk to public function

* per-thread seed and openmp support

* pinsage-like model on movielens

* the code runs now

* support cuda

* using readonly graph

* using C random walk

* removing profile decorators

* param initialization

* no grad

* leaky relu fixes everything

* train and save

* WIP

* WIP

* WIP

* seems to work

* evaluation output

* swapping order of val/test and train

* debug

* hyperparam tuning

* prior/training dataset split changes

* random walk reorg

* random walk with restart

* signed comparison fix

* migrating random walk to nodeflow

* Revert "migrating random walk to nodeflow"

This reverts commit f2565347cced7c912a58a529b257c033d9f375b7.

* add README and remove dataset

* new endpoint

* lint

* lint x2

* oops forgot test

* including bpr - better for baseline

* addressing fixes

* throwing random walks out from SamplerOp class

* forgot to move RandomWalk; why did this even work?

* removing legacy garbage

* add todo

* address comments

* stupid bug fix

* call ndarrayvector converter to handle traces

* use index_select instead of __getitem__

* fix bug in dataset processing

* fix edge_type shape bug

* comments

* refactor graph conv

* docs & tests

* fix lint

* fix lint

* fix lint

* fix lint script

* fix lint

* Update

* Style fix

* Fix style

* Fix style

* Fix gpu case

* Fix for gpu case

* Hotfix edgesoftmax docs

* Handle repeated features

* Add docstring

* Set default arguments

* Remove dropout from nn.conv

* Fix

* add util fn for renaming

* revert gcn_spmv.py

* mx folder

* fix wierd bug

* fix mx

* fix lint

* simple implemention of GraphSAGE

* update the abstract method and `torch.nn` modules are more utilized