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

机器学习算法从数据中学习。训练数据的质量和数量与机器学习项目的成功有关，就像算法本身一样。数据增强方法的目的是引出在训练数据中不容易获得的信息，但需要提高学习系统的性能。数据增强已成功地应用于图像分类：从临时增强，如裁剪，翻转，旋转和“学习增强”的方法。这项工作的重点是图机器学习（GML）的数据增强。GML方法，如图神经网络（GNN）在研究具有重要社会意义的许多类型的图，包括社交网络，分子网络和知识图中发挥了重要作用。虽然增强方法已经应用于GML之前，现有的技术是应用在一个特设的方式，并产生次优的结果。本项目将研究构建计算图的原理方法，增加原始图数据，旨在提高图学习方法的性能。本项目的技术目标分为三个方面。第一个推力开发新的图形机器学习技术，以增加图形数据的反事实推理的边缘作为治疗变量的影响。第二个重点是开发新的图形机器学习技术，通过预测数据中是否存在顺序，时间或动态模式来增强图形。第三个方向是开发新的图机器学习技术，通过伪标记和断开非常不同的社区或集群中的节点来增强图。该项目将提供新颖的方法来增强图形数据，并将各种研究领域的理论和方法结合起来，如统计因果分析，序列建模和预测以及图形挖掘算法。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.