提出首个适用于图结构数据的分布量化方法，解决传统方法在图数据中失效问题。
Adjusted Count Quantification Learning on Graphs
- 引入结构重要性采样，应对图数据中的结构协变量偏移问题
- 设计邻域感知ACC，提升非同质边场景下的量化精度
- 在多个图量化任务上验证有效性，为图数据分布预测提供新思路
量化学习旨在预测一组实例的标签分布。本文研究图结构数据中的量化学习问题，其中实例为节点。以往方法仅依赖节点聚类，但这些方法在图数据上表现不佳。本文将流行的调整分类计数（ACC）方法扩展至图结构，发现其依赖的先验概率偏移假设在图量化中通常不成立。为此，我们提出结构重要性采样（SIS），这是首个可在（结构）协变量偏移下适用的图量化方法。此外，我们提出邻域感知ACC，有效提升非同质边场景下的量化性能。实验表明，所提方法在多个图量化任务上均表现优异。
原文摘要 · Abstract (English)
Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances are vertices. Previously, this problem has only been addressed via node clustering methods. In this paper, we extend the popular Adjusted Classify & Count (ACC) method to graphs. We show that the prior probability shift assumption upon which ACC relies is often not applicable to graph quantification problems. To address this issue, we propose structural importance sampling (SIS), the first graph quantification method that is applicable under (structural) covariate shift. Additionally, we propose Neighborhood-aware ACC, which improves quantification in the presence of non-homophilic edges. We show the effectiveness of our techniques on multiple graph quantification tasks.
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