Tootfinder

Universal Model of Urban Street Networks
https://arxiv.org/abs/2509.21931

Universal Model of Urban Street Networks
Analyzing 9,000 urban areas' street networks, we identify properties, including extreme betweenness centrality heterogeneity, that typical spatial network models fail to explain. Accordingly we propose a universal, parsimonious, generative model based on a two-step mechanism that begins with a spanning tree as a backbone then iteratively adds edges to match empirical degree distributions. Controlled by a single parameter representing lattice-equivalent node density, it accurately reproduces key…

Universal Model of Urban Street Networks
Marc Barthelemy, Geoff Boeing
https://arxiv.org/abs/2509.21931 https://arxiv.org/pdf/2509.21931

Universal Model of Urban Street Networks
Analyzing 9,000 urban areas' street networks, we identify properties, including extreme betweenness centrality heterogeneity, that typical spatial network models fail to explain. Accordingly we propose a universal, parsimonious, generative model based on a two-step mechanism that begins with a spanning tree as a backbone then iteratively adds edges to match empirical degree distributions. Controlled by a single parameter representing lattice-equivalent node density, it accurately reproduces key…

Optimized Sparse Network Coverage via L1-norm Minimization
Souvik Paul, Iv\'an Alexander Morales Sandoval, Giuseppe Thadeu Freitas de Abreu
https://arxiv.org/abs/2509.11994 …

Optimized Sparse Network Coverage via L1-norm Minimization
The selection of nodes that can serve as cluster heads, local sinks and gateways is a critical challenge in distributed sensor and communication networks. This paper presents a novel framework for identifying a minimal set of nexus nodes to ensure full network coverage while minimizing cost. By formulating the problem as a convex relaxation of the NP-hard set cover problem, we integrate the graph theoretic centrality measures of node degree and betweenness centrality into a cost function optimi…

Fast Percolation Centrality Approximation with Importance Sampling
Antonio Cruciani, Leonardo Pellegrina
https://arxiv.org/abs/2509.11454 https://arxiv.org…

Fast Percolation Centrality Approximation with Importance Sampling
In this work we present PercIS, an algorithm based on Importance Sampling to approximate the percolation centrality of all the nodes of a graph. Percolation centrality is a generalization of betweenness centrality to attributed graphs, and is a useful measure to quantify the importance of the vertices in a contagious process or to diffuse information. However, it is impractical to compute it exactly on modern-sized networks. First, we highlight key limitations of state-of-the-art sampling-bas…