1 citations · 1 across the 6 of their papers we have counts for
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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…
Efficient Uniform Negative Edge Weights
Lukas Geis, Daniel Allendorf, Thomas Bläsius +4
We consider a maximum entropy edge weight model that allows for negative weights. Given a graph and possible weights typically consisting of positive and negative…
Structure and Independence in Hyperbolic Uniform Disk Graphs
Thomas Bläsius, Jean-Pierre von der Heydt, Sándor Kisfaludi-Bak +2
We consider intersection graphs of disks of radius in the hyperbolic plane. Unlike the Euclidean setting, these graph classes are different for different values of , where v…
Robust Parameter Fitting to Realistic Network Models via Iterative Stochastic Approximation
Thomas Bläsius, Sarel Cohen, Philipp Fischbeck +2
Random graph models are widely used to understand network properties and graph algorithms. Key to such analyses are the different parameters of each model, which affect various net…