CRII: III: Graph Neural Networks: A Feature and Structure Learning Approach

现实世界中的许多数据可以自然地用图形来表示，例如社交网络、引文网络、化合物和生物网络。图结构是表示项目集合之间关系的一种有效方式。使用图形来表示这些数据，我们可以更深入地了解原始数据中嵌入的复杂数据结构。使用机器学习方法的图挖掘在各个领域产生了许多令人兴奋的发现。现有的图深度学习方法，如图卷积网络(GNN)，通过局部结构运算进行局部信息聚集。它们通常堆叠多个图形卷积层以实现更大的接受域，这是直接的，但可能会导致几个问题，包括过度拟合和过度平滑。这些问题是图神经网络的关键问题，但尚未得到很好的研究。这个项目的目标是开发先进的图形学习方法，而不是深入和复杂的。该项目还促进了将图学习算法整合到现有的本科生和研究生水平的机器学习课程中。具体地说，该项目专注于如何在不深入有效的情况下扩大接受领域。这个项目开发了一组高级的图形卷积、池化和解池化操作。高级图形卷积层利用传送功能在全局范围内选择高度相关的节点。在这一层中，传送函数计算中心节点与本地邻域之外的其他节点之间的相关性。具有特定相关性的节点被传送到中心节点，使得中心节点能够从更广泛的邻域收集信息。新的图池层使用注意操作来生成具有更多图拓扑信息的连接更好的粗化图。具体地说，它利用注意力算子来生成节点选择的排名分数，捕获图的连通性信息。提出的图解池层利用一个注意力算子来初始化恢复的节点。总而言之，该项目提供了关于图形特征和结构学习的系统GNN研究，并有望显著推动GNN的发展。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Many data in the real world can be naturally represented by graphs, such as social networks, citation networks, chemical compounds, and biological networks. The graph structure is an effective way to express relationships among a collection of items. Using graphs to represent these data, we can obtain an advanced understanding of the complex data structures embedded in the raw data. Graph mining using machine learning methods yields many exciting discoveries in various fields. Existing graph deep learning methods, such as graph convolutional networks (GNN), conduct local information aggregation with local structural operations. They usually stack multiple graph convolutional layers to enable a larger receptive field, which is straightforward but can result in several issues, including over-fitting and over-smoothing. These issues are critical to graph neural networks but are not well investigated. The objective of this project is to develop advanced graph learning methods without going deep and complex. This project also facilitates integrating graph learning algorithms into existing curricula of Machine Learning courses at both undergraduate and graduate levels.Specifically, this project focuses on how to enlarge receptive fields without going deep effectively. This project develops a set of advanced convolution, pooling, and un-pooling operations on graphs. The advanced graph convolution layer utilizes teleport functions to select highly relevant nodes at the global scope. In this layer, a teleport function computes relevance between the center node and other nodes beyond the local neighborhood. The nodes with particular relevance are teleported for the center node, enabling the center node to gather information from a broader neighborhood. The new graph pooling layer uses an attention operation to produce a better-connected coarsened graph with more graph topology information. In particular, it leverages an attention operator to generate ranking scores for node selection, capturing the graph connectivity information. The proposed graph un-pooling layer utilizes an attention operator to initialize restored nodes. Altogether, this project provides a systematic GNN study on graph feature and structure learning and is expected to advance GNNs development significantly.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.