III: Small: Comprehensive Methods to Learn to Augment Graph Data

University Of Notre Dame, Notre Dame IN

Investigators

Abstract

Machine learning algorithms learn from data. The quality and quantity of training data has as much to do with the success of machine learning projects as the algorithms themselves. Data augmentation methods aim at eliciting information that is not readily available in the training data, but that is needed to improve the performance of the learning systems. Data augmentation has been successfully used in image classification: from adhoc augmentation, such as cropping, flipping, rotation and 'learning ­to ­augment' methods. This work focuses on data augmentation for Graph Machine Learning (GML). GML methods such as graph neural networks (GNNs) play an important role in studying many types of graphs of significant societal importance, including social networks, molecular networks, and knowledge graphs. While augmentation methods have been applied to GML before, existing techniques are applied in an adhoc manner and yield sub-optimal results. This project will study principled methods for building computational graphs, augmenting raw graph data, aiming at improving the performance of graph learning methods. The technical aims of the project are divided into three thrusts. The first thrust develops novel graph machine learning techniques to augment the graph data by counterfactual inference on the effect of edges as treatment variables. The second thrust develops novel graph machine learning techniques to augment the graph by forecasting if sequential, temporal, or dynamic patterns exist in the data. The third thrust develops novel graph machine learning techniques to augment the graph by pseudo labeling and disconnecting the nodes in very different communities or clusters. This project will deliver novel methods to augment graph data integrating with theories and methods from a variety of research fields, such as statistical causal analysis, sequence modeling and prediction, and graph mining algorithms. It will advance the technologies of graph machine learning and expand the scope of data augmentation for deep learning. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.