Exploring Graph Classification Techniques Under Low Data Constraints: A
  Comprehensive Study
Kush Kothari Bhavya Mehta Reshmika Nambiar Seema Shrawne
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.