Fast Percolation Centrality Approximation with Importance Sampling

Abstract

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.