CSE - Developing New Machine Learning Paradigm Towards Effective yet Efficient Foundation Graph Learning Models

Inspired by the success of foundation language models in applications such as ChatGPT, one can envision the far-reaching impacts that can be brought by a pre-trained Foundation Graph Learning Model (FGLM) with broader applications in the areas such as scientific research, social network analysis, anomaly detection, drug discovery, and e-commerce. Despite the significant progress of pre-trained graph neural networks, there has not yet a FGLM that can achieve desired performance on various graph-learning-related tasks. To bridge this gap, by continuing our effort and with the support of our project recently funded by the NSF, we will design and develop a new machine-learning paradigm (techniques, methods, and models) aiming to jointly solve the cross-task, cross-graph, and cross-domain challenges in graph learning towards effective yet efficient FGLMs, which will help researchers and practitioners in different domains to advance their work in a variety of real-world applications driven by the ubiquitous graph data.