Fast Percolation Centrality Approximation with Importance Sampling
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arXiv
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| Auteurs principaux: | , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866914036598177792 |
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| author | Cruciani, Antonio Pellegrina, Leonardo |
| author_facet | Cruciani, Antonio Pellegrina, Leonardo |
| contents | 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 generalization of betweenness centrality to attributed graphs, and is a useful measure to quantify the importance of the vertices in a contagious process or to diffuse information. However, it is impractical to compute it exactly on modern-sized networks.
First, we highlight key limitations of state-of-the-art sampling-based approximation methods for the percolation centrality, showing that in most cases they cannot achieve accurate solutions efficiently. Then, we propose and analyze a novel sampling algorithm based on Importance Sampling, proving tight sample size bounds to achieve high-quality approximations.
Our extensive experimental evaluation shows that PercIS computes high-quality estimates and scales to large real-world networks, while significantly outperforming, in terms of sample sizes, accuracy and running times, the state-of-the-art. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_11454 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fast Percolation Centrality Approximation with Importance Sampling Cruciani, Antonio Pellegrina, Leonardo Social and Information Networks Data Structures and Algorithms 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 generalization of betweenness centrality to attributed graphs, and is a useful measure to quantify the importance of the vertices in a contagious process or to diffuse information. However, it is impractical to compute it exactly on modern-sized networks. First, we highlight key limitations of state-of-the-art sampling-based approximation methods for the percolation centrality, showing that in most cases they cannot achieve accurate solutions efficiently. Then, we propose and analyze a novel sampling algorithm based on Importance Sampling, proving tight sample size bounds to achieve high-quality approximations. Our extensive experimental evaluation shows that PercIS computes high-quality estimates and scales to large real-world networks, while significantly outperforming, in terms of sample sizes, accuracy and running times, the state-of-the-art. |
| title | Fast Percolation Centrality Approximation with Importance Sampling |
| topic | Social and Information Networks Data Structures and Algorithms |
| url | https://arxiv.org/abs/2509.11454 |