论文标题
用于图形分类的图形神经网络和图形描述符的应用
Application of Graph Neural Networks and graph descriptors for graph classification
论文作者
论文摘要
图形分类是现代研究和行业的重要领域。多种应用，尤其是在化学和新型药物发现方面，可以促进该领域的机器学习模型的快速发展。为了跟上新研究的速度，适当的实验设计，公平评估和独立的基准是必不可少的。强大基准的设计是此类作品必不可少的元素。 在本文中，我们探讨了多种图形分类方法。我们专注于图形神经网络（GNNS），该网络是一种事实上的标准深度学习技术，用于图表表示。还解决了经典方法，例如图形描述符和分子指纹。我们设计公平的评估实验协议，并选择适当的数据集收集。这使我们能够执行众多实验并严格分析现代方法。我们得出了许多结论，这为新算法的性能和质量提供了新的启示。 我们研究了跳跃知识GNN体系结构在图形分类中的应用，这被证明是改善基本图神经网络体系结构的有效工具。还提出了对基线模型的多种改进并通过实验验证，这构成了对公平模型比较领域的重要贡献。
Graph classification is an important area in both modern research and industry. Multiple applications, especially in chemistry and novel drug discovery, encourage rapid development of machine learning models in this area. To keep up with the pace of new research, proper experimental design, fair evaluation, and independent benchmarks are essential. Design of strong baselines is an indispensable element of such works. In this thesis, we explore multiple approaches to graph classification. We focus on Graph Neural Networks (GNNs), which emerged as a de facto standard deep learning technique for graph representation learning. Classical approaches, such as graph descriptors and molecular fingerprints, are also addressed. We design fair evaluation experimental protocol and choose proper datasets collection. This allows us to perform numerous experiments and rigorously analyze modern approaches. We arrive to many conclusions, which shed new light on performance and quality of novel algorithms. We investigate application of Jumping Knowledge GNN architecture to graph classification, which proves to be an efficient tool for improving base graph neural network architectures. Multiple improvements to baseline models are also proposed and experimentally verified, which constitutes an important contribution to the field of fair model comparison.
