Adjusted Count Quantification Learning on Graphs

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 fulfilled and propose two novel graph quantification techniques: Structural importance sampling (SIS) makes ACC applicable in graph domains with covariate shift. Neighborhood-aware ACC improves quantification in the presence of non-homophilic edges. We show the effectiveness of our techniques on multiple graph quantification tasks.

@article{damke2025_2503.09395,
  title={ Adjusted Count Quantification Learning on Graphs },
  author={ Clemens Damke and Eyke Hüllermeier },
  journal={arXiv preprint arXiv:2503.09395},
  year={ 2025 }
}