GitLab

* update

* update

* update demo

* load mxnet csr.

* enable load large csr.

* fix

* fix.

* fix int overflow.

* fix test.

* lazily create msg_index.

* update test.

* add gtest module

* add gtest

* fix

* Update CMakeLists.txt

* Update README.md

* update

* update

* update

* add sender

* update

* remove duplicate cpde

* add test.

* fix compute.

* fix test.

* turn on test.

* fix a bug.

* add test.

* fix.

* disable test.

* fix.

* use inplace.

* move to shared memory graph store.

* fix.

* add more unit tests.

* fix.

* fix test.

* fix test.

* disable test.

* fix.

* 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

* update

* update

* update

* update num_hops

* fix bug

* update

* report numbers of distributed training in AMLC giant graph paper

* WIP

* header

* WIP .cc

* WIP

* transpose

* wip

* immutable graph .h and .cc

* WIP: nodeflow.cc

* compile

* remove all tmp dl managed ctx; they caused refcount issue

* one simple test

* WIP: testing

* test_graph

* fix graph index

* fix bug in sampler; pass pytorch utest

* WIP on mxnet

* fix lint

* fix mxnet unittest w/ unfortunate workaround

* fix msvc

* fix lint

* SliceRows and test_nodeflow

* resolve reviews

* resolve reviews

* try fix win ci

* try fix win ci

* poke win ci again

* poke

* lazy multigraph flag; stackoverflow error

* revert node subgraph test

* lazy object

* try fix win build

* try fix win build

* poke ci

* fix build script

* fix compile

* add a todo

* fix reviews

* fix compile

* add init_ndata and init_edata in DGLGraph.

* adjust SharedMemoryGraph API.

* print warning.

* fix comment.

* update example

* fix.

* fix examples.

* add unit tests.

* add comments.

* fix.

* add comment.

* remove.

* temp fix.

* initialize for shared memory.

* fix graphsage.

* fix gcn.

* add more unit tests.

* add more tests.

* avoid creating shared-memory exclusively.

* redefine remote initializer.

* improve initializer.

* fix unit test.

* fix lint.

* fix lint.

* initialize data in the graph store server properly.

* fix test.

* fix test.

* fix test.

* small fix.

* add comments.

* cleanup server.

* test graph store with a random port.

* print.

* print to stderr.

* test1

* test2

* remove comment.

* adjust the initializer signature.

* Add batch and unbatch for immutable graph

* fix line seprator

* fix lintr

* remove unnecessary include

* fix code review

* Fix inplace operation

* fix line seprator

* add index.

* update.

* update tutorial.

* add sampling tutorial.

* add readme

* update author list.

* fix indent in the code.

* rename the file.

* update tutorial.

* fix the last API.

* update image.

* reorganize sampling code.

* add multi-process training.

* speed up gcn_cv

* fix graphsage_cv.

* add new API in graph store.

* update barrier impl.

* support both local and distributed training.

* fix multiprocess train.

* fix.

* fix barrier.

* add script for loading data.

* multiprocessing sampling.

* accel training.

* replace pull with spmv for speedup.

* nodeflow copy from parent with context.

* enable GPU.

* fix a bug in graph store.

* enable multi-GPU training.

* fix lint.

* add comments.

* rename to run_store_server.py

* fix gcn_cv.

* fix a minor bug in sampler.

* handle error better in graph store.

* improve graphsage_cv for distributed mode.

* update README.

* fix.

* update.

* fix.

* Change to required=True

* Update description

* Change back

* Update 9_gat.py

* Update 1_gcn.py

* Change Distributed Sampler API

* fix lint

* fix lint

* update demo

* update

* update

* update

* update demo

* update demo

* [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

* reorganize sampling code.

* speedup gcn_ns.

* speed up gcn_cv

* fix graphsage_cv.

* undo the modification.

* accel training.

* update readme.

* 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

* disable tests.

* import unittest.

* Refactoring network API

* update demo

* update

* update demo

* update demo

* add num_sender

* update

* fix lint

* fix lint

* fix lint

* update

* add graph classification dataset

* add node label

* add TUDataset

* Modify to consistent with Qi Huang's implementation

* add docs

* Add docs

* Fix change of environment variable

* Update tu.py

* Update tu.py

* Fix error when add node with np.int64

* Fix processing bug

* add graph classification dataset

* add node label

* add TUDataset

* Modify to consistent with Qi Huang's implementation

* add docs

* Add docs

* Fix change of environment variable

* Update tu.py

* Update tu.py

* Fix error when add node with np.int64

* 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