Fast Estimation of Percolation Centrality
<|reference_start|>Fast Estimation of Percolation Centrality: 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 $\varepsilon$ of the value with probability at least $1-\delta$, for fixed constants $\varepsilon,\delta \in (0,1)$. 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.<|reference_end|>
@article{cruciani2024fast,
    title={Fast Estimation of Percolation Centrality},
    author={Antonio Cruciani},
    journal={arXiv preprint arXiv:2408.02389},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.02389},
    primaryClass={cs.SI cs.DS}
}