1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.SI2025
Fast Percolation Centrality Approximation with Importance Sampling
Antonio Cruciani, Leonardo Pellegrina
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 general…
cs.SI2024
Fast Estimation of Percolation Centrality
Antonio Cruciani
In this work, we present a new algorithm to approximate the percolation centrality of every node in a graph. Such a centrality measure quantifies the importance of the vertices in…
cs.DS2023★ 1 cited
MANTRA: Temporal Betweenness Centrality Approximation through Sampling
Antonio Cruciani
We present MANTRA, a framework for approximating the temporal betweenness centrality of all nodes in a temporal graph. Our method can compute probabilistically guaranteed high-qual…