Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

5-2025

Abstract

Graph representation learning has become central to many graph-based tasks, driving advancements in various domains such as web search, recommendation systems, and social network analysis. Traditionally, these methods rely on end-to-end supervised learning paradigms that require abundant labeled data, which can be costly and difficult to obtain. To address this limitation, few-shot learning on graphs has emerged as a promising approach, allowing models to generalize with minimal supervision and overcome data scarcity in real-world applications. This tutorial offers an in-depth exploration of recent advancements in few-shot learning for graphs, providing a comparative analysis of state-of-the-art methods and identifying future research directions. We categorize these approaches into two main taxonomies: (1) a problem taxonomy that examines various types of data scarcity problems and their applications, and (2) a technique taxonomy that outlines key strategies for tackling these challenges, including meta-learning, pre-training methods from both the pre-LLM and LLM eras. The tutorial will conclude by summarizing key insights from the literature and discussing future avenues for research, aiming to equip participants with a deep understanding of few-shot learning on graphs and inspire innovation in this rapidly growing field.

Keywords

Graph Neural Networks, Deep Learning, Large Language Models

Discipline

Graphics and Human Computer Interfaces

Research Areas

Intelligent Systems and Optimization

Areas of Excellence

Digital transformation

Publication

WWW '25: Companion Proceedings of the ACM on Web Conference 2025, Sydney, Australia, April 28 - May 2

First Page

9

Last Page

12

Identifier

10.1145/3701716.3715854

Publisher

ACM

City or Country

New York

Additional URL

https://doi.org/10.1145/3701716.3715854

Download

Share

COinS