Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study

• URL: http://arxiv.org/abs/2311.12737v1
• Date: Tue, 21 Nov 2023 17:23:05 GMT
• Title: Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study
• Authors: Kush Kothari, Bhavya Mehta, Reshmika Nambiar and Seema Shrawne
• Abstract summary: It covers various techniques for graph data augmentation, including node and edge perturbation, graph coarsening, and graph generation. The paper explores these areas in depth and delves into further sub classifications. It provides an extensive array of techniques that can be employed in solving graph processing problems faced in low-data scenarios.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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