Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study
It synthesizes existing research for practitioners facing data scarcity in graph processing, but is incremental as a survey.
This survey paper provides an overview of graph data augmentation and few-shot learning techniques for addressing graph classification problems in low-data scenarios, covering methods like node perturbation and meta-learning without presenting new experimental results.
This survey paper presents a brief overview of recent research on graph data augmentation and few-shot learning. It covers various techniques for graph data augmentation, including node and edge perturbation, graph coarsening, and graph generation, as well as the latest developments in few-shot learning, such as meta-learning and model-agnostic meta-learning. The paper explores these areas in depth and delves into further sub classifications. Rule based approaches and learning based approaches are surveyed under graph augmentation techniques. Few-Shot Learning on graphs is also studied in terms of metric learning techniques and optimization-based techniques. In all, this paper provides an extensive array of techniques that can be employed in solving graph processing problems faced in low-data scenarios.