1 citations · 2 across the 19 of their papers we have counts for
6 papers · 1 filter
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…
Hyperbolic Random Graphs: Clique Number and Degeneracy with Implications for Colouring
Samuel Baguley, Yannic Maus, Janosch Ruff +1
Hyperbolic random graphs inherit many properties that are present in real-world networks. The hyperbolic geometry imposes a scale-free network with a strong clustering coefficient.…
How Many Lines to Paint the City: Exact Edge-Cover in Temporal Graphs
Argyrios Deligkas, Michelle Döring, Eduard Eiben +3
Logistics and transportation networks require a large amount of resources to realize necessary connections between locations and minimizing these resources is a vital aspect of pla…
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…