GitLab

* update metis

* update

* update dataloader

* update dataloader

* new script

* update

* update

* update

* update

* update

* update

* update

* update dataloader

* update

* update

* update

* update

* update

* update

* update

* Add license to every filer header

* 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

* 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

* 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