4 citations · 7 across the 7 of their papers we have counts for
9 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…
Spatial Invasion of Cooperative Parasites
Vianney Brouard, Cornelia Pokalyuk, Marco Seiler +1
In this paper we study invasion probabilities and invasion times of cooperative parasites spreading in spatially structured host populations. The spatial structure of the host popu…
Neural Collapse in Deep Linear Networks: From Balanced to Imbalanced Data
Hien Dang, Tho Tran, Stanley Osher +3
Modern deep neural networks have achieved impressive performance on tasks from image classification to natural language processing. Surprisingly, these complex systems with massive…
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…
Parallel and I/O-efficient Randomisation of Massive Networks using Global Curveball Trades
Corrie Jacobien Carstens, Michael Hamann, Ulrich Meyer +3
Graph randomisation is a crucial task in the analysis and synthesis of networks. It is typically implemented as an edge switching process (ESMC) repeatedly swapping the nodes of ra…