Adjusted Count Quantification Learning on Graphs
This work addresses quantification learning for graph data, which is incremental as it adapts existing methods to a new domain.
The paper tackled the problem of predicting label distributions on graph-structured data by extending the Adjusted Classify & Count method to graphs, proposing two novel techniques to address issues like covariate shift and non-homophilic edges, and demonstrating effectiveness on multiple tasks.
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.