2 papers
cs.AI2026
A Systematic Evaluation Protocol of Graph-Derived Signals for Tabular Machine Learning
Mario Heidrich, Jeffrey Heidemann, Rüdiger Buchkremer +1
While graph-derived signals are widely used in tabular learning, existing studies typically rely on limited experimental setups and average performance comparisons, leaving the sta…
cs.LG2025
ffstruc2vec: Flat, Flexible and Scalable Learning of Node Representations from Structural Identities
Mario Heidrich, Jeffrey Heidemann, Rüdiger Buchkremer +1
Node embedding refers to techniques that generate low-dimensional vector representations of nodes in a graph while preserving specific properties of the nodes. A key challenge in t…