dmlc--dgl
bf264d00fe
* adding LABOR sampling * add ladies and pladies samplers * fix compile error after rebase * add reference for ladies sampler * Improve ladies implementation. * weighted labor sampling initial implementation draft fix indentation and small bug in ladies script * importance_sampling currently doesn't work with weights * fix weighted importance sampling * move labor example into its own folder * lint fixes * Improve documentation * remove examples from the main PR * fix linting by not using c++17 features * fix documentation of labor_sampler.py * update documentation for labor.py * reformat the labor.py file with black * fix linting errors * replace exception use with if * fix typo in error comment * fixing win64 build for ci * fixing weighted implementation, works now. * fix bug in the weighted case and importance_sampling==0 * address part of the reviews * remove unused code paths from cuda * remove unused code path from cpu side * remove extra features of labor making use of random seed. * fix exclude_edges bug * remove pcg and seed logic from cpu implementation, seed logic should still work for cuda. * minor style change * refactor CPU implementation, take out the importance_sampling probability computation into a function. * improve CUDAWorkspaceAllocator * refactor importance_sampling part out to a function * minor optimization * fix linting issue * Revert "remove pcg and seed logic from cpu implementation, seed logic should still work for cuda." This reverts commit c250e07ac6d7e13f57e79e8a2c2f098d777378c2. * Revert "remove extra features of labor making use of random seed." This reverts commit 7f99034353080308f4783f27d9a08bea343fb796. * fix the documentation * disable NIDs * improve the documentation in the code * use the stream argument in pcg32 instead of skipping ahead t times, can discard the use of hashmap now since it is faster this way. * fix linting issue * address another round of reviews * further optimize CPU LABOR sampling implementation * fix linting error * update the comment * reformat * rename and rephrase comment * fix formatting according to new linting specs * fix compile error due to renaming, fix linting. * lint * rename DGLHeteroGraph to DGLGraph to match master * replace other occurrences of DGLHeteroGraph to DGLGraph Co-authored-by: Muhammed Fatih BALIN <m.f.balin@gmail.com> Co-authored-by: Kaan Sancak <kaansnck@gmail.com> Co-authored-by: Quan Gan <coin2028@hotmail.com>
3.6 KiB
3.6 KiB
Contributing to DGL
Contribution is always welcomed. A good starting place is the roadmap issue, where you can find our current milestones. All contributions must go through pull requests and be reviewed by the committers. See our contribution guide for more details.
Once your contribution is accepted and merged, congratulations, you are now a contributor to the DGL project. We will put your name in the list below.
Contributors
- Minjie Wang from AWS
- Da Zheng from AWS
- Quan Gan from AWS
- Mufei Li from AWS
- Jinjing Zhou from AWS
- Xiang Song from AWS
- Tianjun Xiao from AWS
- Tong He from AWS
- Jian Zhang from AWS
- Qipeng Guo from AWS
- Xiangkun Hu from AWS
- Ying Rui from AWS
- Israt Nisa from AWS
- Zheng Zhang from AWS
- Zihao Ye from University of Washington
- Chao Ma
- Qidong
- Lingfan Yu from New York University
- Yu Gai from University of California, Berkeyley
- Qi Huang from New York University
- Dominique LaSalle from Nvidia
- Pawel Piotrowcz from Intel
- Michal Szarmach from Intel
- Izabela Mazur from Intel
- Sanchit Misra from Intel
- Sheng Zha from AWS
- Yifei Ma from AWS
- Yizhi Liu from AWS
- Kay Liu from UIC
- Tianqi Zhang from SJTU
- Hengrui Zhang
- Seung Won Min from UIUC
- @hbsun2113: GraphSAGE in PyTorch
- Tianyi Zhang: SGC in PyTorch
- Jun Chen: GIN in PyTorch
- Aymen Waheb: APPNP in PyTorch
- Chengqiang Lu: MGCN, SchNet and MPNN in PyTorch
- Gongze Cao: Cluster GCN
- Yicheng Wu: RotatE in PyTorch
- Hao Xiong: DeepWalk in PyTorch
- Zhi Lin: Integrate FeatGraph into DGL
- Andrew Tsesis: Framework-Agnostic Graph Ops
- Brett Koonce
- @giuseppefutia
- @mori97
- @xnuohz
- Hao Jin from Amazon
- Xin Yao from Nvidia
- Abdurrahman Yasar from Nvidia
- Shaked Brody from Technion
- Jiahui Liu from Nvidia
- Neil Dickson from Nvidia
- Chang Liu from Nvidia
- Muhammed Fatih Balin from Nvidia and Georgia Tech