[TPAMI 2022] "Bag of Tricks for Training Deeper Graph Neural Networks A Comprehensive Benchmark Study" by Tianlong Chen*, Kaixiong Zhou*, Keyu Duan, Wenqing Zheng, Peihao Wang, Xia Hu, Zhangyang Wang

Reimplementation of Graph Autoencoder by Kipf & Welling with DGL.

Locally Private Graph Neural Networks (ACM CCS 2021)

Seamlessly build the MuMiN dataset.

Course: Graph Machine Learning focuses on the application of machine learning algorithms on graph-structured data. Some of the key topics that are covered in the course include graph representation learning and graph neural networks, algorithms for the world wide web, reasoning over knowledge graphs, and social network analysis.

Program Translator AI built on Pytorch

Pipeline for Aspect-Based Sentiment Analysis of texts

Implement, test, and organize recent reseach of GNN-based methods. Enable lifecycle controlled with MLflow.

🌐 Explore Graph Neural Networks through this comprehensive course, covering essential architectures and cutting-edge research in deep learning.

Semester 6: NAM Project. A recommender system using link prediction algorithms and GCN.

Social Computing with Deep Graph Library (DGL)

Graph Attention Networks (GATs) for node classification and regression tasks