Graph Few-shot Learning with Task-specific Structures

TL;DR

This paper introduces a novel graph few-shot learning framework that learns task-specific structures for each meta-task, improving node classification performance by tailoring representations to individual tasks.

Contribution

It proposes a new method to learn task-specific graph structures for each meta-task, addressing limitations of existing GNN-based few-shot learning approaches.

Findings

Outperforms state-of-the-art baselines on five node classification datasets.

Effective in both single- and multiple-graph settings.

Demonstrates the importance of task-specific structures in graph few-shot learning.

Abstract

Graph few-shot learning is of great importance among various graph learning tasks. Under the few-shot scenario, models are often required to conduct classification given limited labeled samples. Existing graph few-shot learning methods typically leverage Graph Neural Networks (GNNs) and perform classification across a series of meta-tasks. Nevertheless, these methods generally rely on the original graph (i.e., the graph that the meta-task is sampled from) to learn node representations. Consequently, the graph structure used in each meta-task is identical. Since the class sets are different across meta-tasks, node representations should be learned in a task-specific manner to promote classification performance. Therefore, to adaptively learn node representations across meta-tasks, we propose a novel framework that learns a task-specific structure for each meta-task. To handle the variety of nodes across meta-tasks, we extract relevant nodes and learn task-specific structures based on node influence and mutual information. In this way, we can learn node representations with the task-specific structure tailored for each meta-task. We further conduct extensive experiments on five node classification datasets under both single- and multiple-graph settings to validate the superiority of our framework over the state-of-the-art baselines. Our code is provided at https://github.com/SongW-SW/GLITTER.