Graph deep learning aims to apply deep learning techniques to learn from complex data that is represented as graph. In recent years, due to the rising trends in network analysis and prediction, Graph Neural Networks (GNNs) as a powerful deep learning approach have been widely applied in various fields, e.g., object recognition, image classification, and semantic segmentation. However, graphs are in irregular non-Euclidean domains. This brings up the challenge of how to design deep learning techniques in order to effectively extract useful features from arbitrary graphs. This project will investigate the state-of-the-art techniques of GNNs and develop new technioques to improve their limitations.
graph neural networks, graph kernels, graph algorithms, machine learning