Graph regression is a fundamental task and has received increasing attention
in a wide range of graph learning tasks. However, the inference process is
often not interpretable. Most existing explanation techniques are limited to
understanding GNN behaviors in classification tasks. In this work, we seek an
explanation to interpret the graph regression models (XAIG-R). We show that
existing methods overlook the distribution shifting and continuously ordered
decision boundary, which hinders them away from being applied in the regression
tasks. To address these challenges, we propose a novel objective based on the
information bottleneck theory and introduce a new mix-up framework, which could
support various GNNs in a model-agnostic manner. We further present a
contrastive learning strategy to tackle the continuously ordered labels in
regression task. To empirically verify the effectiveness of the proposed
method, we introduce three benchmark datasets and a real-life dataset for
evaluation. Extensive experiments show the effectiveness of the proposed method
in interpreting GNN models in regression tasks.

本文提出了一种基于信息瓶颈理论的目标函数和混合框架来解释解释图回归模型（XAIG-R）的方法，并引入了对比学习策略来解决连续有序标签的问题，实验证明该方法在解释图神经网络模型中的回归任务方面具有有效性。

RegExplainer: 生成回归任务图神经网络解释

RegExplainer: Generating Explanations for Graph Neural Networks in  Regression Task

Active learning has long been a topic of study in machine learning. However,
as increasingly complex and opaque models have become standard practice, the
process of active learning, too, has become more opaque. There has been little
investigation into interpreting what specific trends and patterns an active
learning strategy may be exploring. This work expands on the Local
Interpretable Model-agnostic Explanations framework (LIME) to provide
explanations for active learning recommendations. We demonstrate how LIME can
be used to generate locally faithful explanations for an active learning
strategy, and how these explanations can be used to understand how different
models and datasets explore a problem space over time. In order to quantify the
per-subgroup differences in how an active learning strategy queries spatial
regions, we introduce a notion of uncertainty bias (based on disparate impact)
to measure the discrepancy in the confidence for a model's predictions between
one subgroup and another. Using the uncertainty bias measure, we show that our
query explanations accurately reflect the subgroup focus of the active learning
queries, allowing for an interpretable explanation of what is being learned as
points with similar sources of uncertainty have their uncertainty bias
resolved. We demonstrate that this technique can be applied to track
uncertainty bias over user-defined clusters or automatically generated clusters
based on the source of uncertainty.

该论文扩展了 LIME 框架来为主动学习的建议提供可解释性的解释，并引入了不确定性偏差的概念来衡量主动学习策略查询空间区域的子组差异。使用该技术可以跟踪用户定义的聚类或基于不确定性来源自动生成的聚类。