Cogdl

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.

**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](https://zhuanlan.zhihu.com/p/395622791). 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](https://kdd2021graph.github.io/) 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.

**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](https://kdd2021graph.github.io/) 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

**A new major release!** 🎉🎉🎉
It provides a fast spmm operator to speed up GNN training. We also release the first version of [CogDL paper](https://arxiv.org/abs/2103.00959) in arXiv. In the paper, we introduce the design, the characteristics, the features, and the reproducible leaderboards.
Welcome to join [our slack](https://join.slack.com/t/cogdl/shared_invite/zt-b9b4a49j-2aMB035qZKxvjV4vqf0hEg)!

New Features
- 193 Support ge-spmm for fast GNN training
- 171 Add [configs](https://github.com/THUDM/cogdl/blob/master/cogdl/configs.py) for reproducible leaderboards
- 161 Add attributed graph clustering task
- 161 Add self-supervised auxiliary task
- 187 188 Add OAGBert v2 and [its usage](https://github.com/THUDM/cogdl/blob/master/cogdl/oag/README.md)
- 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

**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`](https://github.com/THUDM/cogdl/blob/master/cogdl/experiments.py) API (see [`examples/quick_start.py`](https://github.com/THUDM/cogdl/blob/master/examples/quick_start.py) for reference)
- 151 Enable `automl` feature in [`experiment`](https://github.com/THUDM/cogdl/blob/master/cogdl/experiments.py) API, the usage is in [README](https://github.com/THUDM/cogdl#api-usage)
- 157 Add `pipeline` API (see [`examples/pipeline.py`](https://github.com/THUDM/cogdl/blob/master/examples/pipeline.py) for reference)
- 153 Add [`oagbert`](https://github.com/THUDM/cogdl/tree/master/cogdl/oag) API (see [`examples/oagbert.py`](https://github.com/THUDM/cogdl/blob/master/examples/oagbert.py) for reference)
- 59 Add similarity search task
- 78 Add [neighborhood sampler](https://github.com/THUDM/cogdl/blob/e4f952322460369d0503743ec5a6f25b2316c339/cogdl/trainers/sampled_trainer.py#L139) for large-scale training
- 87 Apply GNN for [link prediction](https://github.com/THUDM/cogdl/blob/c897d79bbaf8f4aa92eb9e0f47f7c0a2e1756c47/cogdl/tasks/link_prediction.py#L446) task

New Models
- 67 Add [`SGC`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/sgc.py) model (thanks to KHTee)
- 60 Add [`SGC-PN`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/sgc_pn.py) model (thanks to feng-y16)
- 63 Add [`PPNP`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/ppnp.py) model (thanks to TiagoMAntunes)
- 68 Add [`SAGPool`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/sagpool.py) model (thanks to frouioui)
- 69 Add [`GDC_GCN`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/gdc_gcn.py) model (thanks to kwyoke)
- 74 Add [`JKNet`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/dgl_jknet.py) (thanks to WXR1998)
- 76 Add [`SIGN`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/sign.py) model (thanks to hmartelb)
- 80 Add [`HGP-SL`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/hgp_sl/py) model (thanks to Sahandfer)
- 88 Add [`DropEdge`](https://github.com/THUDM/cogdl/tree/master/cogdl/models/nn/dropedge_gcn.py) model (thanks to JiaYiLiJayee)
- 96 Add [`Graph U-Net`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/nn/pyg_graph_unet.py) model
- 102 Add [`PPRGo`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/nn/pprgo.py) model

New Datasets
- 158 Add [Yelp/Amazon](https://github.com/THUDM/cogdl/blob/master/cogdl/datasets/saint_data.py) datasets in this [paper](https://arxiv.org/pdf/1907.04931.pdf).

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](https://github.com/THUDM/cogdl/blob/master/cogdl/trainers/self_supervised_trainer.py)

New Features
- 48 Support the [pre-training](https://github.com/THUDM/cogdl/blob/master/cogdl/tasks/pretrain.py) task on [molecule graphs](https://github.com/THUDM/cogdl/blob/master/cogdl/models/nn/pyg_stpgnn.py)
- 38 Add [`Trainer`](https://github.com/THUDM/cogdl/tree/master/cogdl/trainers) API for flexible training
- 38 Add [`Sampler`](https://github.com/THUDM/cogdl/blob/master/cogdl/trainers/sampled_trainer.py) API for training large-scale datasets and now supports `GraphSAINT` sampler.

New Models
- 48 [`STP-GNN`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/nn/pyg_stpgnn.py) for pre-training
- 38 [`GPT-GNN`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/nn/pyg_gpt_gnn.py) for node classification
- 39 Triple based knowledge embedding methods ([`complex`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/emb/complext.py), [`distmult`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/emb/distmult.py), [`rotate`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/emb/rotate.py), [`transe`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/emb/transe.py))
- 48 [`DeeperGCN`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/nn/pyg_deepergcn.py) for node classification
- 48 [`GCNII`](https://github.com/THUDM/cogdl/blob/master/cogdl/models/nn/gcnii.py) 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](https://github.com/THUDM/cogdl/blob/master/cogdl/datasets/pyg_strategies_data.py)
- "bio" and "chem" in [`Jure's paper`](https://openreview.net/pdf?id=HJlWWJSFDH).
- BBBP and BACE
- 38 Add [OGB](https://github.com/THUDM/cogdl/blob/master/cogdl/datasets/pyg_ogb.py) 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`](https://ogb.stanford.edu/#)

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