CogDL v0.6

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

New Contributors

• @xll2001 made their first contribution in #347
• @Kinseys made their first contribution in #357
• @Diego0511 made their first contribution in #358
• @Saltsmart made their first contribution in #361
• @hwangyeong made their first contribution in #392
• @Renxs177 made their first contribution in #407

Full Changelog: v0.5.3...v0.6

CogDL v0.5.3

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

Full Changelog: v0.5.2...v0.5.3

CogDL v0.5.2

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 #314
• [Dataset] Update geom datasets by @cenyk1230 in #315
• [Bugfix] Fix set device by @cenyk1230 in #316
• [Bugfix] Fix data memory by @cenyk1230 in #317
• [Example] Add clustergcn for ogbn by @cenyk1230 in #320
• [Doc] Prepare v0.5.2 release by @cenyk1230 in #322

Full Changelog: v0.5.1...v0.5.2

CogDL v0.5.1

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 #312
• [Operator] new scatter_max by @fishmingyu in #308
• [Dataset] Add more datasets by @cenyk1230 in #313

Full Changelog: v0.5.0...v0.5.1

CogDL v0.5.0

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

New Contributors

• @huangtinglin made their first contribution in #305
• @jasmine-yu made their first contribution in #309

Full Changelog: 0.4.1...v0.5.0

CogDL v0.5.0-alpha1

Release 0.5.0-alpha1

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.

CogDL v0.5.0-alpha0

Release 0.5.0-alpha0

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.

CogDL v0.4.1

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 v0.4.0

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 v0.3.0

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