Exploration-Aided Downstream Graph Learning Tasks: A Survey on Exploratory Graph Learning

Yu Hou, Won-Yong Shin

Abstract: Graph learning is crucial for extracting meaningful information from graph-structured data, enabling effective solutions to various downstream tasks. However, existing methods for solving downstream graph learning tasks often rely on the availability of the graph structure, which may not always be accessible in real-world applications. To overcome this limitation, recent approaches have introduced exploratory learning techniques, which aim to tackle graph learning tasks on graphs with unknown topology. In this article, we provide a comprehensive overview of exploratory graph learning applied to two widely studied graph learning tasks: 1) influence maximization and 2) community detection. We delve into the problem formulation of both tasks concerning graphs with unknown topological information. Additionally, we explore the application of exploratory learning techniques to address these problems effectively.