17 citations · 47 across the 10 of their papers we have counts for
12 papers
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,…
Certifying Induced Subgraphs in Large Graphs
Ulrich Meyer, Hung Tran, Konstantinos Tsakalidis
We introduce I/O-optimal certifying algorithms for bipartite graphs, as well as for the classes of split, threshold, bipartite chain, and trivially perfect graphs. When the input g…
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…
Simulating Population Protocols in Sub-Constant Time per Interaction
Petra Berenbrink, David Hammer, Dominik Kaaser +3
We consider the problem of efficiently simulating population protocols. In the population model, we are given a distributed system of agents modeled as identical finite-state m…
Recent Advances in Scalable Network Generation
Manuel Penschuck, Ulrik Brandes, Michael Hamann +5
Random graph models are frequently used as a controllable and versatile data source for experimental campaigns in various research fields. Generating such data-sets at scale is a n…