Bot releases are hidden (Show)

CogDL - CogDL v0.6 Latest Release

Published by cenyk1230 over 1 year ago

The new v0.6 release updates the tutorials and adds more examples, such as GraphMAE, GraphMAE2, and BGRL.

What's Changed

Update doc tutorials by @QingFei1 in https://github.com/THUDM/cogdl/pull/352, https://github.com/THUDM/cogdl/pull/355
Integrate GRB by @xll2001 in https://github.com/THUDM/cogdl/pull/347
Add dgraph-cogdl in examples by @Kinseys in https://github.com/THUDM/cogdl/pull/357
Update README by @cenyk1230 in https://github.com/THUDM/cogdl/pull/364
update ogbl datasets by @Diego0511 in https://github.com/THUDM/cogdl/pull/358
update APIs for gensim 4.x by @Saltsmart in https://github.com/THUDM/cogdl/pull/361
Update Triple_Link_Prediction by @QingFei1 in https://github.com/THUDM/cogdl/pull/371
Small Changes by @QingFei1 in https://github.com/THUDM/cogdl/pull/374, https://github.com/THUDM/cogdl/pull/381
Update Graphsage/Unsup_Graphsage by @QingFei1 in https://github.com/THUDM/cogdl/pull/379, https://github.com/THUDM/cogdl/pull/384, https://github.com/THUDM/cogdl/pull/425
Fix bugs in oagbert.encode_paper by @THINK2TRY in https://github.com/THUDM/cogdl/pull/385
Revise for GCC by @hwangyeong in https://github.com/THUDM/cogdl/pull/392
Update GRB by @cenyk1230 in https://github.com/THUDM/cogdl/pull/406
Add stgcn code for traffic prediction task by @Renxs177 in https://github.com/THUDM/cogdl/pull/407
BGRL with CogDL by @hwangyeong in https://github.com/THUDM/cogdl/pull/408
GCC update by @hwangyeong in https://github.com/THUDM/cogdl/pull/409
Add GraphMAE by @cenyk1230 in https://github.com/THUDM/cogdl/pull/428
Add GraphMAE2 by @cenyk1230 in https://github.com/THUDM/cogdl/pull/429

New Contributors

@xll2001 made their first contribution in https://github.com/THUDM/cogdl/pull/347
@Kinseys made their first contribution in https://github.com/THUDM/cogdl/pull/357
@Diego0511 made their first contribution in https://github.com/THUDM/cogdl/pull/358
@Saltsmart made their first contribution in https://github.com/THUDM/cogdl/pull/361
@hwangyeong made their first contribution in https://github.com/THUDM/cogdl/pull/392
@Renxs177 made their first contribution in https://github.com/THUDM/cogdl/pull/407

Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.3...v0.6

CogDL - CogDL v0.5.3

Published by cenyk1230 over 2 years ago

Release 0.5.3

The CogDL v0.5.3 release supports mixed-precision training by setting fp16=True and provides a basic example written by Jittor. It also updates the tutorial in the document, fixes downloading links of some datasets, and fixes potential bugs of operators.

What's Changed

[Dataset] Update rd2cd datasets by @cenyk1230 in https://github.com/THUDM/cogdl/pull/323
[Feature] Support fp16 by @cenyk1230 in https://github.com/THUDM/cogdl/pull/325
[Bugfix] Fix copying args by @cenyk1230 in https://github.com/THUDM/cogdl/pull/326
[Example] Add GAT for ogbn-arxiv dataset by @cenyk1230 in https://github.com/THUDM/cogdl/pull/327
[Enhancement] Merge parallel training by @cenyk1230 in https://github.com/THUDM/cogdl/pull/332
[Bugfix] Fix dgk/graph2vec/gdc/grace by @cenyk1230 in https://github.com/THUDM/cogdl/pull/335
[Dependency] Fix numpy version by @cenyk1230 in https://github.com/THUDM/cogdl/pull/338
[Dataset] Update download links by @cenyk1230 in https://github.com/THUDM/cogdl/pull/346
[Doc] Update doc tutorials by @cenyk1230 in https://github.com/THUDM/cogdl/pull/348
[Bugfix] Fix edge softmax by @cenyk1230 in https://github.com/THUDM/cogdl/pull/349
[Feature] Jittor gcn example by @cenyk1230 in https://github.com/THUDM/cogdl/pull/350
[Doc] Prepare v0.5.3 release by @cenyk1230 in https://github.com/THUDM/cogdl/pull/351

Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.2...v0.5.3

CogDL - CogDL v0.5.2

Published by cenyk1230 almost 3 years ago

Release 0.5.2

The CogDL 0.5.2 release adds a GNN example for ogbn-products and updates geom datasets. It also fixes some potential bugs including setting devices, using cpu for inference, etc.

What's Changed

[Bugfix] Fix packing operator files by @cenyk1230 in https://github.com/THUDM/cogdl/pull/314
[Dataset] Update geom datasets by @cenyk1230 in https://github.com/THUDM/cogdl/pull/315
[Bugfix] Fix set device by @cenyk1230 in https://github.com/THUDM/cogdl/pull/316
[Bugfix] Fix data memory by @cenyk1230 in https://github.com/THUDM/cogdl/pull/317
[Example] Add clustergcn for ogbn by @cenyk1230 in https://github.com/THUDM/cogdl/pull/320
[Doc] Prepare v0.5.2 release by @cenyk1230 in https://github.com/THUDM/cogdl/pull/322

Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.1...v0.5.2

CogDL - CogDL v0.5.1

Published by cenyk1230 almost 3 years ago

Release 0.5.1

The CogDL 0.5.1 release adds fast operators including SpMM (cpu version) and scatter_max (cuda version). It also adds lots of datasets for node classification which can be found in this link.

What's Changed

[Feature] Add fast spmm (cpu) by @cenyk1230 in https://github.com/THUDM/cogdl/pull/312
[Operator] new scatter_max by @fishmingyu in https://github.com/THUDM/cogdl/pull/308
[Dataset] Add more datasets by @cenyk1230 in https://github.com/THUDM/cogdl/pull/313

Full Changelog: https://github.com/THUDM/cogdl/compare/v0.5.0...v0.5.1

CogDL - CogDL v0.5.0

Published by cenyk1230 almost 3 years ago

Release 0.5.0

The CogDL 0.5.0 release focuses on modular design and ease of use. It designs and implements a unified training loop for GNN, which introduces DataWrapper to help prepare the training/validation/test data and ModelWrapper to define the training/validation/test steps.

What's Changed

[Bugfix] Fix MoEGCN & actnn import by @cenyk1230 in https://github.com/THUDM/cogdl/pull/271
[Notebook] Add notebooks by @cenyk1230 in https://github.com/THUDM/cogdl/pull/276
[Paperlist] 100 GNN papers by @cenyk1230 in https://github.com/THUDM/cogdl/pull/277
[Framework] Unify the GNN training loop by @cenyk1230 in https://github.com/THUDM/cogdl/pull/285
[Framework] Remove register models/datasets/wrappers by @cenyk1230 in https://github.com/THUDM/cogdl/pull/286
[Pipeline] Fix pipeline by @cenyk1230 in https://github.com/THUDM/cogdl/pull/289
[Custom] Fix model name by @cenyk1230 in https://github.com/THUDM/cogdl/pull/290
[Docs] Update docs & examples by @cenyk1230 in https://github.com/THUDM/cogdl/pull/292
[Docs] Fix building docs by @cenyk1230 in https://github.com/THUDM/cogdl/pull/293
[Dataset] Update ogb arxiv & Fix epochs by @cenyk1230 in https://github.com/THUDM/cogdl/pull/294
[Custom] Fix custom wrappers by @cenyk1230 in https://github.com/THUDM/cogdl/pull/295
[Dataset] Add geom datasets by @cenyk1230 in https://github.com/THUDM/cogdl/pull/296
[Model] Add fused GAT by @cenyk1230 in https://github.com/THUDM/cogdl/pull/297
[Submodule] Add FastMoE as third-party library by @cenyk1230 in https://github.com/THUDM/cogdl/pull/298
[Model] Move pyg models to examples by @cenyk1230 in https://github.com/THUDM/cogdl/pull/299
[Bugfix] Fix sample adj by @cenyk1230 in https://github.com/THUDM/cogdl/pull/301
[DATASET] Add description for datasets by @THINK2TRY in https://github.com/THUDM/cogdl/pull/304
[Utility] Update spmm utils by @cenyk1230 in https://github.com/THUDM/cogdl/pull/303
[Model] VRGCN example by @huangtinglin in https://github.com/THUDM/cogdl/pull/305
[Utility] Update spmm utils by @cenyk1230 in https://github.com/THUDM/cogdl/pull/306
[Bugfix] Update loading datasets by @cenyk1230 in https://github.com/THUDM/cogdl/pull/307
[Feature] Support AutoGNN by @jasmine-yu in https://github.com/THUDM/cogdl/pull/309
[Bugfix] Fix GAT's NaN by @cenyk1230 in https://github.com/THUDM/cogdl/pull/310

New Contributors

@huangtinglin made their first contribution in https://github.com/THUDM/cogdl/pull/305
@jasmine-yu made their first contribution in https://github.com/THUDM/cogdl/pull/309

Full Changelog: https://github.com/THUDM/cogdl/compare/0.4.1...v0.5.0

CogDL - CogDL v0.5.0-alpha1

Published by cenyk1230 almost 3 years ago

Release 0.5.0-alpha1

CogDL - CogDL v0.5.0-alpha0

Published by cenyk1230 almost 3 years ago

Release 0.5.0-alpha0

CogDL - CogDL v0.4.1

Published by THINK2TRY about 3 years ago

A new release! 🎉🎉🎉
In the new v0.4.1 release, CogDL implements multiple deepgnn models and we also give a analysis of deepgnn in Chinese. Now CogDL. supports both reversible and actnn for memory efficiency to help build super deep GNNs. Come and have a try. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this link for more details. 🎉

New Features

#230 Add new tasks for OAGBert, including zero-shot inference and supervised classification
#243 #251 Add new pipelines of GenerateEmbeddingPipeline
#248 Add recommendation task
#249 Separate layers from models for users to build custom models more conveniently.
#256 Add message-passing base framework.
#262 #263 #266 Supports actnn in graph neural networks
#266 Add message-passing ops implemented in Python

New Models

#258 Add c&s(correct and smooth) and SAGN
#260 #261 Add RevGNN wrappers and models (revgcn, revgat, revgen)

New Datasets

#230 Add datasest for OAGBert: l0fos, aff30, arxivvenue.

New Examples

#265 Implements HGNN using CogDL.

Bug Fixes

#237 #240 Fix bugs in calling ge-spmm and using Graph
#238 Modify examples of gnns to adapt to cogdl.Graph.
#257 Fix bugs in ogb datasets and moe-gcn
#259 Fix bugs in calling cusparse API.

Docs

#242 Add a brief tutorial for CogDL.

CogDL - CogDL v0.4.0

Published by cenyk1230 over 3 years ago

A new major release! 🎉🎉🎉
The new v0.4.0 release refactors the data storage (from Data to Graph) and provides more fast operators to speed up GNN training. It also includes many self-supervised learning methods on graphs. BTW, we are glad to announce that we will give a tutorial on KDD 2021 in August. Please see this link for more details. 🎉

New Features

Reformat Data Storage (from Data to Graph), edge_index from torch.Tensor to tuple(Tensor, Tensor). The inputs of each GNN are unified as one parameter graph.
#205 #210 #212 Add SDDMM operator
#234 Add multi-head SpMM operator and speed up edge_softmax.
#211 #222 Support distributed training

New Models

#207 Add MoEGNN Model
#213 #220 OAG-Bert (Chinese versions)
#217 #235 Add self-supervised models

New Datasets

#226 Add ogbn-mag dataset

New Examples

#233 Add Simple-HGN model

Bug Fixes

#209 Fix STPGNN and heterogeneous task
#225 Fix TUDataset

CogDL - CogDL v0.3.0

Published by cenyk1230 over 3 years ago

A new major release! 🎉🎉🎉
It provides a fast spmm operator to speed up GNN training. We also release the first version of CogDL paper in arXiv. In the paper, we introduce the design, the characteristics, the features, and the reproducible leaderboards.
Welcome to join our slack!

New Features

#193 Support ge-spmm for fast GNN training
#171 Add configs for reproducible leaderboards
#161 Add attributed graph clustering task
#161 Add self-supervised auxiliary task
#187 #188 Add OAGBert v2 and its usage
#184 #186 #199 Update leaderboards

New Models

#193 Add ClusterGCN model
#194 Add GraphSAINT model

New Datasets

#167 Add Reddit dataset
#175 Add PPI dataset

New Examples

#173 Add usages of customized models
#174 Add usages of customized datasets

Miscellaneous

#170 Remove PyG dependency of several models
#169 #174 #182 Remove PyG dependency of datasets

CogDL - CogDL v0.2.0

Published by cenyk1230 almost 4 years ago

A new major release!! It includes easy-to-use experiment and pipeline APIs for all experiments and applications. It also provides oagbert API. Thanks to all the contributors 🎉

New Features

#142 Add experiment API (see examples/quick_start.py for reference)
#151 Enable automl feature in experiment API, the usage is in README
#157 Add pipeline API (see examples/pipeline.py for reference)
#153 Add oagbert API (see examples/oagbert.py for reference)
#59 Add similarity search task
#78 Add neighborhood sampler for large-scale training
#87 Apply GNN for link prediction task

New Models

#67 Add SGC model (thanks to @KHTee)
#60 Add SGC-PN model (thanks to @feng-y16)
#63 Add PPNP model (thanks to @TiagoMAntunes)
#68 Add SAGPool model (thanks to @frouioui)
#69 Add GDC_GCN model (thanks to @kwyoke)
#74 Add JKNet (thanks to @WXR1998)
#76 Add SIGN model (thanks to @hmartelb)
#80 Add HGP-SL model (thanks to @Sahandfer)
#88 Add DropEdge model (thanks to @JiaYiLiJayee)
#96 Add Graph U-Net model
#102 Add PPRGo model

New Datasets

#158 Add Yelp/Amazon datasets in this paper.

Bug Fixes

#141 Fix bugs when using CPU

Requirement Update

CogDL now requires numba
CogDL now requires transformers

Document Update

#140 Update the structure of the document
#143~#147 Fix readthedocs build

Miscellaneous

#61 Introduce Code style (thanks to @MaLiN2223)
#66 Create dockerfile for CogDL (thanks to @TiagoMAntunes)
#86 Add a script for contributing a new model (thanks to @Sahandfer)
#133 Add templates for github issues and pull requests
#135 Integrate the training and evaluation of self-supervised models with a trainer

CogDL - CogDL v0.1.2

Published by cenyk1230 almost 4 years ago

New Features

#48 Support the pre-training task on molecule graphs
#38 Add Trainer API for flexible training
#38 Add Sampler API for training large-scale datasets and now supports GraphSAINT sampler.

New Models

#48 STP-GNN for pre-training
#38 GPT-GNN for node classification
#39 Triple based knowledge embedding methods (complex, distmult, rotate, transe)
#48 DeeperGCN for node classification
#48 GCNII for node classification

New Results

#51 Update the leaderboard of the unsupervised node classification task
#48 Update the leaderboard of the semi-supervised node classification task
#48 Update the leaderboard of the graph classification task

New Datasets

#50 Add some molecule datasets
- "bio" and "chem" in Jure's paper.
- BBBP and BACE
#38 Add OGB datasets

New Examples

#51 Add many examples of embedding methods
#48 Add many examples of graph neural networks

Requirement Update

#38 CogDL now requires ogb

Miscellaneous

#50 #54 Remove saved/ folder and support downloading pre-trained GCC model
#52 Improve the coverage to 80%

CogDL - CogDL v0.1.1

Published by cenyk1230 about 4 years ago

New Features

Support link prediction task on knowledge graphs
Support hyper-parameter search using optuna

New Models

GCC for graph classification: GCC is a contrastive learning framework that implements unsupervised structural graph representation pre-training.
GRAND for node classification (thanks to @wzfhaha): GRAND randomly drops node features in training process to implement data augmentatoin and achieves sota in benchmarks.
DGI for unsupervised node classification: DGI applies local-global contrastive learning methods to train GNN and first achieves results comparable to semi-supervised methods in benchmarks.
MVGRL for unsupervised node classification: MVGRL is a self-supervised approach based on contrastive multi-view learning to learn representations.
ProNE++ for unsupervised node classification: ProNE++ employs graph filter and AutoML to help enhance node embeddings.
GraphSAGE for unsupervised node classification: unsupervised version of GraphSAGE.
DisenGCN for node classification: DisenGCN disentangles node representations by separating different factors.
CompGCN/RGCN for KG link prediction: RGCN and CompGCN are GNNs for knowledge graph embedding considering the type of edges.