Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Schaller, Ulysse"'
Autor:
Kaufmann, Marc, Lakis, Kostas, Lengler, Johannes, Ravi, Raghu Raman, Schaller, Ulysse, Sturm, Konstantin
We study push-pull rumour spreading in small-world models for social networks where the degrees follow a power-law. In a non-geometric setting Fountoulakis, Panagiotou and Sauerwald have shown that rumours always spread fast (SODA 2012). On the other
Externí odkaz:
http://arxiv.org/abs/2408.01268
The introduction of geometry has proven instrumental in the efforts towards more realistic models for real-world networks. In Geometric Inhomogeneous Random Graphs (GIRGs), Euclidean Geometry induces clustering of the vertices, which is widely observ
Externí odkaz:
http://arxiv.org/abs/2405.19369
The compact Genetic Algorithm (cGA), parameterized by its hypothetical population size $K$, offers a low-memory alternative to evolving a large offspring population of solutions. It evolves a probability distribution, biasing it towards promising sam
Externí odkaz:
http://arxiv.org/abs/2404.12147
Recently there has been increased interest in fitting generative graph models to real-world networks. In particular, Bl\"asius et al. have proposed a framework for systematic evaluation of the expressivity of random graph models. We extend this frame
Externí odkaz:
http://arxiv.org/abs/2402.03837
In this paper we study a version of (non-Markovian) first passage percolation on graphs, where the transmission time between two connected vertices is non-iid, but increases by a penalty factor polynomial in their expected degrees. Based on the expon
Externí odkaz:
http://arxiv.org/abs/2309.11880
Publikováno v:
Leibniz International Proceedings in Informatics (LIPIcs), 145
In the Maximum Label Propagation Algorithm (Max-LPA), each vertex draws a distinct random label. In each subsequent round, each vertex updates its label to the label that is most frequent among its neighbours (including its own label), breaking ties
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c3e06ffd33efae5de3a2e4e94294bf31