4 citations · 5 across the 3 of their papers we have counts for
4 papers · 1 filter
Distribution Matching for Graph Quantification Under Structural Covariate Shift
Clemens Damke, Eyke Hüllermeier
Graphs are commonly used in machine learning to model relationships between instances. Consider the task of predicting the political preferences of users in a social network; to so…
Adjusted Count Quantification Learning on Graphs
Clemens Damke, Eyke Hüllermeier
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 a…
CUQ-GNN: Committee-based Graph Uncertainty Quantification using Posterior Networks
Clemens Damke, Eyke Hüllermeier
In this work, we study the influence of domain-specific characteristics when defining a meaningful notion of predictive uncertainty on graph data. Previously, the so-called Graph P…
A Novel Higher-order Weisfeiler-Lehman Graph Convolution
Clemens Damke, Vitalik Melnikov, Eyke Hüllermeier
Current GNN architectures use a vertex neighborhood aggregation scheme, which limits their discriminative power to that of the 1-dimensional Weisfeiler-Lehman (WL) graph isomorphis…