* 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 code for mxnet TAGCov
* Update some docs
* Fix some code
* Update docs dgl.nn.mxnet
* Update weight init
* 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
* 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 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+.
* 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
* 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
* 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
xiang song(charlie.song)