2024 [ nach oben ]

Harder, Jonathan Gadea; Kötzing, Timo; Li, Xiaoyue; Radhakrishnan, Aishwarya; Ruff, Janosch Run Time Bounds for Integer-Valued OneMax FunctionsGenetic and Evolutionary Computation Conference (GECCO ’24) 2024
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@conference{2024bounds, author = {Harder, Jonathan Gadea and Kötzing, Timo and Li, Xiaoyue and Radhakrishnan, Aishwarya and Ruff, Janosch}, booktitle = {Genetic and Evolutionary Computation Conference (GECCO '24)}, keywords = {sys:relevantfor:ae aishwaryaradhakrishnan gecco janoschruff jonathangadeaharder timokoetzing xiaoyueli year2024}, title = {Run Time Bounds for Integer-Valued OneMax Functions}, year = 2024 }

2023 [ nach oben ]

Bläsius, Thomas; Friedrich, Tobias; Katzmann, Maximilian; Ruff, Janosch; Zeif, Ziena On the Giant Component of Geometric Inhomogeneous Random GraphsEuropean Symposium on Algorithms (ESA) 2023: 20:1–20:13
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In this paper we study the threshold model of geometric inhomogeneous random graphs (GIRGs); a generative random graph model that is closely related to hyperbolic random graphs (HRGs). These models have been observed to capture complex real-world networks well with respect to the structural and algorithmic properties. Following comprehensive studies regarding their connectivity, i.e., which parts of the graphs are connected, we have a good understanding under which circumstances a giant component (containing a constant fraction of the graph) emerges. While previous results are rather technical and challenging to work with, the goal of this paper is to provide more accessible proofs. At the same time we significantly improve the previously known probabilistic guarantees, showing that GIRGs contain a giant component with probability \( 1 - exp(-\Omega (n^{(3-\tau )/2})) \) for graph size \(n\) and a degree distribution with power-law exponent \(\tau \in (2, 3)\). Based on that we additionally derive insights about the connectivity of certain induced subgraphs of GIRGs.
@inproceedings{blasius2023giant, abstract = {In this paper we study the threshold model of geometric inhomogeneous random graphs (GIRGs); a generative random graph model that is closely related to hyperbolic random graphs (HRGs). These models have been observed to capture complex real-world networks well with respect to the structural and algorithmic properties. Following comprehensive studies regarding their connectivity, i.e., which parts of the graphs are connected, we have a good understanding under which circumstances a giant component (containing a constant fraction of the graph) emerges. While previous results are rather technical and challenging to work with, the goal of this paper is to provide more accessible proofs. At the same time we significantly improve the previously known probabilistic guarantees, showing that GIRGs contain a giant component with probability \( 1 - exp(-\Omega (n^{(3-\tau )/2})) \) for graph size \(n\) and a degree distribution with power-law exponent \(\tau \in (2, 3)\). Based on that we additionally derive insights about the connectivity of certain induced subgraphs of GIRGs.}, author = {Bläsius, Thomas and Friedrich, Tobias and Katzmann, Maximilian and Ruff, Janosch and Zeif, Ziena}, booktitle = {European Symposium on Algorithms (ESA)}, keywords = {esa janoschruff maximiliankatzmann thomasblaesius tobiasfriedrich year2023 zienazeif}, pages = {20:1–20:13}, title = {On the Giant Component of Geometric Inhomogeneous Random Graphs}, year = 2023 }