In this work, we address semi-supervised classification of graph data, where
the categories of those unlabeled nodes are inferred from labeled nodes as well
as graph structures. Recent works often solve this problem via advanced graph
convolution in a conventionally supervised manner, but the performance could
degrade significantly when labeled data is scarce. To this end, we propose a
Graph Inference Learning (GIL) framework to boost the performance of
semi-supervised node classification by learning the inference of node labels on
graph topology. To bridge the connection between two nodes, we formally define
a structure relation by encapsulating node attributes, between-node paths, and
local topological structures together, which can make the inference
conveniently deduced from one node to another node. For learning the inference
process, we further introduce meta-optimization on structure relations from
training nodes to validation nodes, such that the learnt graph inference
capability can be better self-adapted to testing nodes. Comprehensive
evaluations on four benchmark datasets (including Cora, Citeseer, Pubmed, and
NELL) demonstrate the superiority of our proposed GIL when compared against
state-of-the-art methods on the semi-supervised node classification task.

本文提出一种用于半监督图节点分类的 “图推理学习”(GIL) 框架，通过学习节点标签的推理，提高分类性能，定义节点属性、节点路径和局部拓扑结构之间的结构关系，从而方便地从一个节点推导出另一个节点的推理，通过在训练节点到验证节点上进行结构关系的元优化，使学习到的图推理能力更好地适应测试节点。在四个基准数据集上进行了全面评估，结果表明，与半监督节点分类任务中最先进的方法相比，我们提出的 GIL 方法具有卓越的性能。