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Maximilian Katzmann

2 accepted papers

2025

Weighted Embeddings for Low-Dimensional Graph Representation

AAAI 2025technical

Learning low-dimensional numerical representations from symbolic data, e.g., embedding the nodes of a graph into a geometric space, is an important concept in machine learning. While embedding into Euclidean space is common, recent observations indicate that hyperbolic geometry is better suited to r…

2024

Real-World Networks Are Low-Dimensional: Theoretical and Practical Assessment

IJCAI 2024poster

Recent empirical evidence suggests that real-world networks have very low underlying dimensionality. We provide a theoretical explanation for this phenomenon as well as develop a linear-time algorithm for detecting the underlying dimensionality of such networks. Our theoretical analysis consid…