1 citations · 1 across the 11 of their papers we have counts for
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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…