[2603.01941] BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop

Computer Science > Machine Learning arXiv:2603.01941 (cs) [Submitted on 2 Mar 2026] Title:BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop Authors:Chao Chen, Xujia Li, Dongsheng Hong, Shanshan Lin, Xiangwen Liao, Chuanyi Liu, Lei Chen View a PDF of the paper titled BAED: a New Paradigm for Few-shot Graph Learning with Explanation in the Loop, by Chao Chen and 6 other authors View PDF HTML (experimental) Abstract:The challenges of training and inference in few-shot environments persist in the area of graph representation learning. The quality and quantity of labels are often insufficient due to the extensive expert knowledge required to annotate graph data. In this context, Few-Shot Graph Learning (FSGL) approaches have been developed over the years. Through sophisticated neural architectures and customized training pipelines, these approaches enhance model adaptability to new label distributions. However, compromises in \textcolor{black}{the model's} robustness and interpretability can result in overfitting to noise in labeled data and degraded performance. This paper introduces the first explanation-in-the-loop framework for the FSGL problem, called BAED. We novelly employ the belief propagation algorithm to facilitate label augmentation on graphs. Then, leveraging an auxiliary graph neural network and the gradient backpropagation method, our framework effectively extracts explanatory subgraphs surrounding target nodes. The final predictions a...