Graph Few-shot Learning with Task-specific Structures

• URL: http://arxiv.org/abs/2210.12130v1
• Date: Fri, 21 Oct 2022 17:40:21 GMT
• Title: Graph Few-shot Learning with Task-specific Structures
• Authors: Song Wang, Chen Chen, Jundong Li
• Abstract summary: Existing graph few-shot learning methods typically leverage Graph Neural Networks (GNNs) We propose a novel framework that learns a task-specific structure for each meta-task. In this way, we can learn node representations with the task-specific structure tailored for each meta-task.
• Score: 38.52226241144403
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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