4 papers
Weighted Embeddings for Low-Dimensional Graph Representation
Thomas Bläsius, Jean-Pierre von der Heydt, Maximilian Katzmann +1
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. Whi…
Maximal Cliques in Scale-Free Random Graphs
Thomas Bläsius, Maximillian Katzmann, Clara Stegehuis
We investigate the number of maximal cliques, i.e., cliques that are not contained in any larger clique, in three network models: ErdÅs-Rényi random graphs, inhomogeneous random…
Real-World Networks are Low-Dimensional: Theoretical and Practical Assessment
Tobias Friedrich, Andreas Göbel, Maximilian Katzmann +1
Detecting the dimensionality of graphs is a central topic in machine learning. While the problem has been tackled empirically as well as theoretically, existing methods have severa…
Cliques in High-Dimensional Geometric Inhomogeneous Random Graphs
Tobias Friedrich, Andreas Göbel, Maximilian Katzmann +1
A recent trend in the context of graph theory is to bring theoretical analyses closer to empirical observations, by focusing the studies on random graph models that are used to rep…