11 citations · 24 across the 10 of their papers we have counts for
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cs.SI2026
Sampling Random Graphs from the Colored Configuration Model
Leonardo Pellegrina
A fundamental step in knowledge discovery is statistically assessing data mining results. In network analysis, such evaluation compares the outcome of a given procedure with the ou…
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.SI2023★ 11 cited
Efficient Centrality Maximization with Rademacher Averages
Leonardo Pellegrina
The identification of the set of k most central nodes of a graph, or centrality maximization, is a key task in network analysis, with various applications ranging from finding comm…