CogDL v0.2.0

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