Few-Shot Learning on Graphs: Meta-learning, Pre-training, and Hybrid Approaches

Keskeiset käsitteet

The author explores the advancements in few-shot learning on graphs through meta-learning, pre-training, and hybrid approaches to address the challenge of limited labeled data availability. The survey categorizes existing studies into three major families and outlines future research directions.

Tiivistelmä

The content delves into the evolution of graph representation learning techniques for few-shot learning. It discusses meta-learning, pre-training, and hybrid approaches in detail. The survey categorizes these techniques, highlights their strengths and limitations, and proposes future research avenues.

Graph representation learning has seen significant advancements with the emergence of few-shot learning on graphs. Earlier techniques relied heavily on ample labeled data for end-to-end training. Few-shot learning addresses this constraint by operating with only a few task-specific labels available for each task. The content systematically categorizes existing studies into three major families: meta-learning approaches, pre-training approaches, and hybrid approaches. Within each category, relationships among methods are analyzed to compare strengths and limitations.

Meta-learning methods derive a prior from a series of "meta-training" tasks mirroring downstream "meta-testing" tasks. They aim to learn a general adaptation process known as "learning-to-learn." Pre-training approaches optimize self-supervised pretext tasks on unlabeled graph data to train a graph encoder capturing task-agnostic intrinsic knowledge in graphs. Hybrid approaches integrate both meta-learning and pre-training paradigms to leverage their respective strengths effectively.

The survey outlines potential future directions for few-shot learning on graphs, including addressing challenges related to large-scale graphs, complex graph structures, cross-domain graphs, improving interpretability of models, developing foundation models on graphs for diverse applications across various domains and task types.

Tilastot

Earlier techniques rely heavily on ample labeled data for end-to-end training.
Few-shot learning operates with only a few task-specific labels available for each task.
Meta-learning methods derive a prior from a series of "meta-training" tasks mirroring downstream "meta-testing" tasks.
Pre-training approaches optimize self-supervised pretext tasks on unlabeled graph data to train a graph encoder.
Hybrid approaches integrate both meta-learning and pre-training paradigms effectively.

Lainaukset

"The success of such techniques often depend on extensive labeled data for end-to-end training."
"Few-shot learning addresses this constraint by operating with only a few task-specific labels available for each task."
"Meta-learning methods derive a prior from a series of 'meta-training' tasks mirroring downstream 'meta-testing' tasks."
"Pre-training approaches optimize self-supervised pretext tasks on unlabeled graph data to train a graph encoder."
"Hybrid approaches integrate both meta-learning and pre-training paradigms effectively."

Tärkeimmät oivallukset

Few-Shot Learning on Graphs

by Xingtong Yu,... klo arxiv.org 02-29-2024

https://arxiv.org/pdf/2402.01440.pdf

Syvällisempiä Kysymyksiä

How can we address diverse range of few-shot learning tasks without extensively annotated base set

To address a diverse range of few-shot learning tasks without an extensively annotated base set, one promising approach is to leverage pre-training on graphs. By utilizing self-supervised methods on unlabeled graph data, a graph encoder can be trained to capture task-agnostic intrinsic knowledge in the pre-training stage. This prior knowledge can then be transferred to various downstream tasks through an adaptation stage. Additionally, parameter-efficient adaptation strategies like prompt tuning and adapter tuning can help tailor the initial model to specific tasks without updating all parameters during fine-tuning. These techniques allow for more efficient utilization of limited labeled data in few-shot scenarios while still benefiting from insights gained from meta-learning approaches.

What are the implications of using black-box models in interpreting results in few-shot learning

The use of black-box models in interpreting results in few-shot learning poses significant challenges when it comes to understanding and explaining the reasoning behind predictions. In many cases, these models lack transparency and make it difficult for users or stakeholders to comprehend how decisions are made. This lack of interpretability can lead to issues with trust, accountability, and bias detection within the system. To overcome this limitation, it is crucial to develop techniques that provide insights into the rationale behind predictions for few-shot tasks on graphs. Designing more interpretable prompts on graphs that explicitly explain their relationship to node features or graph structures could enhance transparency and facilitate better decision-making based on model outputs.

How can we enhance transferability across different domains in pre-trained graph models

Enhancing transferability across different domains in pre-trained graph models requires addressing domain shift challenges on cross-domain graphs as well as bridging gaps created by heterogeneity in graph structures. One key aspect involves unifying complex structures such as 3D graphs, multi-modal graphs, and dynamic graphs under a common framework that enables seamless transfer of knowledge between different types of structures.
Additionally, improving transferability across task types beyond traditional link prediction or node classification entails extending pre-trained models' capabilities to handle regression tasks, graph editing operations, or even generation tasks like molecule-to-text transformations effectively.
By focusing on developing universal feature spaces that accommodate various domain-specific characteristics while ensuring consistent performance across diverse applications and task types will significantly enhance transferability across different domains in pre-trained graph models.