1 citations · 1 across the 1 of their papers we have counts for
2 papers
cs.LG2025
A Remedy for Over-Squashing in Graph Learning via Forman-Ricci Curvature based Graph-to-Hypergraph Structural Lifting
Michael Banf, Dominik Filipiak, Max Schattauer +1
Graph Neural Networks are highly effective at learning from relational data, leveraging node and edge features while maintaining the symmetries inherent to graph structures. Howeve…
cs.LG2025★ 1 cited
Tripartite-GraphRAG via Plugin Ontologies
Michael Banf, Johannes Kuhn
Large Language Models (LLMs) have shown remarkable capabilities across various domains, yet they struggle with knowledge-intensive tasks in areas that demand factual accuracy, e.g.…