Fast Estimation of Percolation Centrality
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916378804486144 |
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| author | Cruciani, Antonio |
| author_facet | Cruciani, Antonio |
| contents | 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 $1-δ$, for fixed constants $\varepsilon,δ\in (0,1)$. 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_02389 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fast Estimation of Percolation Centrality Cruciani, Antonio Social and Information Networks Data Structures and Algorithms 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 $1-δ$, for fixed constants $\varepsilon,δ\in (0,1)$. 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. |
| title | Fast Estimation of Percolation Centrality |
| topic | Social and Information Networks Data Structures and Algorithms |
| url | https://arxiv.org/abs/2408.02389 |