Tootfinder

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…

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…