  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.
