2024/08/05 by Antonio Cruciani, Cruciani, Antonio
Computer Science · Physics and Astronomy · #Bayesian Methods and Mixture Models #Complex Network Analysis Techniques #Data Structures and Algorithms (cs.DS) #FOS: Computer and information sciences #Opinion Dynamics and Social Influence #Social and Information Networks (cs.SI)
paper · pdf · doi:10.48550/arxiv.2408.02389
openalex publication_date 2024/08/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 a network during a contagious process. In this paper, we present a randomized approximation algorithm that can compute probabilistically guaranteed high-quality percolation centrality estimates, generalizing techniques used by Pellegrina and Vandin (TKDD 2024) for the betweenness centrality. The estimation obtained by our algorithm is within ε of the value with probability at least 1-δ, for fixed constants ε,δ∈ (0,1). We our theoretical results with an extensive experimental analysis on several real-world networks and provide empirical evidence that our algorithm improves the current state of the art in speed, and sample size while maintaining high accuracy of the percolation centrality estimates.