v0.4.0 release

GAMMA Lab officially released version 0.4 of GammaGL today. You can download the latest gammaGL from the OpenI community, GitHub, or through pip.

New Additions

• Add CaGCN model based on confidence correction [NeurIPS 2021] (#157)
• Add Graph structure learning model CoGSL [WWW 2022] (#170)
• Add DR-GST model for data distribution shift [WWW 2022] (#161)
• Add spectral domain self-attention model SpecFormer [ICLR 2023] (#172)
• Add Adaptive multi-channel graph convolutional network model AM-GCN [KDD 2020] (#184)
• Add graph contrastive "provable training" model GNN-POT [NeurIPS 2023] (#171)
• Add graph contrastive learning model GGD based on group discrimination [NeurIPS 2022] (#181)
• Add spectral cotrastive learning model Grace-SpCo [NeurIPS 2022] (#189)
• Add knowledge distillation framework LTD [WSDM 2022] (#186)
• Add Graphormer based on Transformer [NeurIPS 2021] (#193)
• Add homogeneous graph datasets WikiCS and PolBlogs and BlogCatalog (#176)
• Add dataset for molecular machine learning MoleculeNet (#192)
• Optimize the issue of console output redundant content when training the model (#183)
• Add .clang-format to adjust C++ code format (#182)
• Added Github Action to help users verify the correctness when submitting code (#191)

Bugs

• Fixe the problem that HGB dataset cannot be downloaded (#167)
• Fix the abnormal edge feature problem of tu dataset (commit 6fabdfd)
• Fix data type issue in Reddit dataset (commit c3c06d8)
• Fixed the problem that the RGCN model does not support the latest version of Numpy (#183)
• Fix the calculation error of segment operator during backpropagation (#173)

Support Latest PyTorch and MindSpore

Currently, GammaGL supports PyTorch version 2.1.2 and MindSpore version 2.2.10.

We have made compatibility fixes for TensorLayerX. For PyTorch, GammaGL supports not only old versions before 1.10, but also the latest version 2.1.2. The relevant TensorLayerX repository link is https://github.com/dddg617/TensorLayerX/tree/nightly.

Database Support

The latest version of GammaGL adds support for graph databases. We developed the graph database interface gdbi, which implements a unified standard interface for four graph databases (neo4j, NebulaGraph, AtlasGraph, and g-store). You can get the example here (#194). The relevant repository link is https://github.com/xy-Ji/gdbi.

v0.3.0 release

1. New models
2. Faster message passing APIs
3. Renew the computation structure with pybind11
4. Dataset cache

v0.2.0 release

1. New models
2. New datasets
3. Faster API
4. GPU sample and heterogeneous graph sample

GammaGL v0.1.0

[docs] update docs

GammaGL v0.0.1

[Model]fix model bugs (#71)

* [Model]fix init

* [Model]fix model bugs