1 citations · 1 across the 11 of their papers we have counts for
12 papers
A Tight Epidemic Threshold for Competing Stochastic Infection Processes with Mutually Exclusive Immunity
Nicolas Klodt, Martin S. Krejca
Stochastic infection processes are continuous-time Markov chains on graphs that assign each vertex one of multiple states, such as susceptible, infected, or recovered. Depending on…
Reemergence of the Epidemic Threshold in SIRS Infections on Connected Stars
Andreas Göbel, Nicolas Klodt, Martin S. Krejca
The SIRS process is a continuous-time process for how infections spread on a graph. In this model, each vertex is in one of the following three states: susceptible (to the infectio…
Temporal Network Creation Games: The Impact of Flexible Labels
Hans Gawendowicz, Nicolas Klodt, Aleksandrs Morgensterns +1
A crucial aspect of research is understanding how real-world networks, such as transportation and information networks, are formed. A prominent model for such networks was introduc…
Temporal Exploration of Random Spanning Tree Models
Samuel Baguley, Andreas Göbel, Nicolas Klodt +3
The Temporal Graph Exploration problem (TEXP) takes as input a temporal graph, i.e., a sequence of graphs on the same vertex set, and asks for a walk of s…
Gradually Declining Immunity Retains the Exponential Duration of Immunity-Free Diffusion
Andreas Göbel, Nicolas Klodt, Martin S. Krejca +1
Diffusion processes pervade numerous areas of AI, abstractly modeling the dynamics of exchanging, oftentimes volatile, information in networks. A central question is how long the i…
Dynamic Network Discovery via Infection Tracing
Ben Bals, Michelle Döring, Nicolas Klodt +1
Researchers, policy makers, and engineers need to make sense of data from spreading processes as diverse as rumor spreading in social networks, viral infections, and water contamin…
Catch Me If You Can: Finding the Source of Infections in Temporal Networks
Ben Bals, Michelle Döring, Nicolas Klodt +1
Source detection (SD) is the task of finding the origin of a spreading process in a network. Algorithms for SD help us combat diseases, misinformation, pollution, and more, and hav…
Temporal Network Creation Games: The Impact of Non-Locality and Terminals
Davide Bilò, Sarel Cohen, Tobias Friedrich +4
We live in a world full of networks where our economy, our communication, and even our social life crucially depends on them. These networks typically emerge from the interaction o…
How to Reduce Temporal Cliques to Find Sparse Spanners
Sebastian Angrick, Ben Bals, Tobias Friedrich +7
Many real-world networks, such as transportation or trade networks, are dynamic in the sense that the edge set may change over time, but these changes are known in advance. This be…
From Market Saturation to Social Reinforcement: Understanding the Impact of Non-Linearity in Information Diffusion Models
Tobias Friedrich, Andreas Göbel, Nicolas Klodt +2
Diffusion of information in networks is at the core of many problems in AI. Common examples include the spread of ideas and rumors as well as marketing campaigns. Typically, inform…
Temporal Network Creation Games
Davide Bilò, Sarel Cohen, Tobias Friedrich +4
Most networks are not static objects, but instead they change over time. This observation has sparked rigorous research on temporal graphs within the last years. In temporal graphs…
Analysis of the survival time of the SIRS process via expansion
Tobias Friedrich, Andreas Göbel, Nicolas Klodt +2
We study the SIRS process, a continuous-time Markov chain modeling the spread of infections on graphs. In this model, vertices are either susceptible, infected, or recovered. Each…