  Node classification is of great importance among various graph mining tasks.
In practice, real-world graphs generally follow the long-tail distribution,
where a large number of classes only consist of limited labeled nodes. Although
Graph Neural Networks (GNNs) have achieved significant improvements in node
classification, their performance decreases substantially in such a few-shot
scenario. The main reason can be attributed to the vast generalization gap
between meta-training and meta-test due to the task variance caused by
different node/class distributions in meta-tasks (i.e., node-level and
class-level variance). Therefore, to effectively alleviate the impact of task
variance, we propose a task-adaptive node classification framework under the
few-shot learning setting. Specifically, we first accumulate meta-knowledge
across classes with abundant labeled nodes. Then we transfer such knowledge to
the classes with limited labeled nodes via our proposed task-adaptive modules.
In particular, to accommodate the different node/class distributions among
meta-tasks, we propose three essential modules to perform \emph{node-level},
\emph{class-level}, and \emph{task-level} adaptations in each meta-task,
respectively. In this way, our framework can conduct adaptations to different
meta-tasks and thus advance the model generalization performance on meta-test
tasks. Extensive experiments on four prevalent node classification datasets
demonstrate the superiority of our framework over the state-of-the-art
baselines. Our code is provided at https://github.com/SongW-SW/TENT.
