Doc: add a DGL-powered projects DGLD (#4254)

* feature: add a parse parameter degree_as_nlabel for pytorch-gin demo

* fix some typo

* [fix]: allow to benchmark all of the 9 dataset.

* [Feature] add epoch number to log

* [Feature]:simply list the command lines for all datasets (https://github.com/dmlc/dgl/pull/3676#discussion_r790270705) and run a test.

* Update README.md

* update: DGL-powered projects-DGLD

[DGLD](https://github.com/EagleLab-ZJU/DGLD

) is an open-source library for Deep Graph Anomaly Detection based on pytorch and DGL.  It provides unified interface of popular graph anomaly detection methods, including the data loader, data augmentation, model training and evaluation. Also, the widely used modules are well organized so that developers and researchers can quickly implement their own designed models.
Co-authored-by: Ubuntu <ubuntu@ip-172-31-10-175.ap-northeast-1.compute.internal>
Co-authored-by: Mufei Li <mufeili1996@gmail.com>

README.md

@@ -85,7 +85,7 @@ Take the survey [here](https://forms.gle/Ej3jHCocACmb49Gp8) and leave any feedba
 * TGL: A graph learning framework for large-scale temporal graphs. https://github.com/amazon-research/tgl
 * gtrick: Bag of Tricks for Graph Neural Networks. https://github.com/sangyx/gtrick
 * ArangoDB-DGL Adapter: Import [ArangoDB](https://github.com/arangodb/arangodb) graphs into DGL and vice-versa. https://github.com/arangoml/dgl-adapter
-
+* DGLD: [DGLD](https://github.com/EagleLab-ZJU/DGLD) is an open-source library for Deep Graph Anomaly Detection based on pytorch and DGL.
 ### Awesome Papers Using DGL
 
 1. [**Benchmarking Graph Neural Networks**](https://arxiv.org/pdf/2003.00982.pdf), *Vijay Prakash Dwivedi, Chaitanya K. Joshi, Thomas Laurent, Yoshua Bengio, Xavier Bresson*