4 citations · 5 across the 5 of their papers we have counts for
6 papers
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…
Uniform Generation of Temporal Graphs with Given Degrees
Daniel Allendorf
Uniform sampling from the set of graphs with a given degree-sequence is a classical problem in the study…
Maintaining Discrete Probability Distributions in Practice
Daniel Allendorf
A classical problem in random number generation is the sampling of elements from a given discrete distribution. Formally, given a set of indices and sequence…
Parallel and I/O-Efficient Algorithms for Non-Linear Preferential Attachment
Daniel Allendorf, Ulrich Meyer, Manuel Penschuck +1
Preferential attachment lies at the heart of many network models aiming to replicate features of real world networks. To simulate the attachment process, conduct statistical tests,…
Parallel Global Edge Switching for the Uniform Sampling of Simple Graphs with Prescribed Degrees
Daniel Allendorf, Ulrich Meyer, Manuel Penschuck +1
The uniform sampling of simple graphs matching a prescribed degree sequence is an important tool in network science, e.g. to construct graph generators or null-models. Here, the Ed…
Engineering Uniform Sampling of Graphs with a Prescribed Power-law Degree Sequence
Daniel Allendorf, Ulrich Meyer, Manuel Penschuck +2
We consider the following common network analysis problem: given a degree sequence return a uniform sample from the ensemble of all…