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arXiv · 2311.12737

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

Kush Kothari·Bhavya Mehta·Reshmika Nambiar·Seema Shrawne
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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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Kush Kothari, Bhavya Mehta, Reshmika Nambiar, Seema Shrawne. 2023-11-21. Exploring Graph Classification Techniques Under Low Data Constraints: A Comprehensive Study. https://doi.org/10.1109/icccnt56998.2023.10307388

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