Rank the spreading influence of nodes using dynamic Markov process

TL;DR

This paper introduces a dynamic Markov process method that accurately evaluates and ranks the spreading influence of nodes in networks by modeling outbreak sizes during epidemic processes.

Contribution

The paper presents a novel DMP method combining Markov chains with spreading models to improve outbreak size estimation and node influence ranking.

Findings

DMP outperforms previous methods in accuracy

Effective for both single and multiple spreaders

Applicable to static and temporal networks

Abstract

Ranking the spreading influence of nodes is of great importance in practice and research. The key to ranking a node's spreading ability is to evaluate the fraction of susceptible nodes been infected by the target node during the outbreak, i.e., the outbreak size. In this paper, we present a dynamic Markov process (DMP) method by integrating the Markov chain and the spreading process to evaluate the outbreak size of the initial spreader. Following the idea of the Markov process, this method solves the problem of nonlinear coupling by adjusting the state transition matrix and evaluating the probability of the susceptible node being infected by its infected neighbours. We have employed the susceptible-infected-recovered (SIR) and susceptible-infected-susceptible (SIS) models to test this method on real-world static and temporal networks. Our results indicate that the DMP method could evaluate the nodes' outbreak sizes more accurately than previous methods for both single and multi-spreaders. Besides, it can also be employed to rank the influence of nodes accurately during the spreading process.