Semi-supervised node classification on graphs is an important research
problem, with many real-world applications in information retrieval such as
content classification on a social network and query intent classification on
an e-commerce query graph. While traditional approaches are largely
transductive, recent graph neural networks (GNNs) integrate node features with
network structures, thus enabling inductive node classification models that can
be applied to new nodes or even new graphs in the same feature space. However,
inter-graph differences still exist across graphs within the same domain. Thus,
training just one global model (e.g., a state-of-the-art GNN) to handle all new
graphs, whilst ignoring the inter-graph differences, can lead to suboptimal
performance.
In this paper, we study the problem of inductive node classification across
graphs. Unlike existing one-model-fits-all approaches, we propose a novel
meta-inductive framework called MI-GNN to customize the inductive model to each
graph under a meta-learning paradigm. That is, MI-GNN does not directly learn
an inductive model; it learns the general knowledge of how to train a model for
semi-supervised node classification on new graphs. To cope with the differences
across graphs, MI-GNN employs a dual adaptation mechanism at both the graph and
task levels. More specifically, we learn a graph prior to adapt for the
graph-level differences, and a task prior to adapt for the task-level
differences conditioned on a graph. Extensive experiments on five real-world
graph collections demonstrate the effectiveness of our proposed model.

本文介绍了一种名为 MI-GNN 的基于元学习范式的元 + 归纳框架，以定制归纳模型以适应每个图表的方法。 MI-GNN 在图和任务级别上都采用双适应机制，对来自同一领域内不同的图表进行自适应，提高了半监督节点分类的性能。