Which Node Properties Identify the Propagation Source in Networks?

Malignant propagation events in networks, such as large-scale diffusion of computer viruses, rumors and failures, have caused massive damage to our society. Thus, it is critical to study how to identify the propagation source. However, existing source identification algorithms only quantify the impact mechanisms of part of the factors that affect the Maximum Likelihood Estimator (MLE) of propagation source, which result in reduced source identification accuracy. In this paper, through constructing a mathematical model for propagation process, we derive two node properties, called Average Eccentricity and Infection Force, which quantify the impact mechanisms of all the factors that affect the MLE of propagation source. And then, we design an AEIF source identification algorithm based on the above two node properties, which make AEIF algorithm has improved accuracy and lower time complexity than existing algorithm. Finally, in the experimental part, extensive simulations on various synthetic networks and real-world networks demonstrate the outperformance of AEIF algorithm than existing algorithms, and based on the experimental results, some assignment suggestions of parameters in AEIF algorithm are given.