Fu Dongqi, He Jingrui
Department of Computer Science, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
School of Information Sciences, University of Illinois at Urbana-Champaign, Champaign, IL, United States.
Front Big Data. 2022 Dec 2;5:1062637. doi: 10.3389/fdata.2022.1062637. eCollection 2022.
Graph structures have attracted much research attention for carrying complex relational information. Based on graphs, many algorithms and tools are proposed and developed for dealing with real-world tasks such as recommendation, fraud detection, molecule design, etc. In this paper, we first discuss three topics of graph research, i.e., graph mining, graph representations, and graph neural networks (GNNs). Then, we introduce the definitions of and in graphs, and the related works of natural and artificial dynamics about how they boost the aforementioned graph research topics, where we also discuss the current limitation and future opportunities.
图结构因能够承载复杂的关系信息而备受研究关注。基于图，人们提出并开发了许多算法和工具来处理诸如推荐、欺诈检测、分子设计等现实世界任务。在本文中，我们首先讨论图研究的三个主题，即图挖掘、图表示和图神经网络（GNN）。然后，我们介绍图中[此处原文缺失具体内容]的定义，以及关于它们如何推动上述图研究主题的自然和人工动力学的相关工作，在此我们也讨论了当前的局限性和未来的机遇。
