Abstract
Locating the source that undergoes a diffusion-like process is a fundamental and challenging problem in complex network, which can help inhibit the outbreak of epidemics among humans, suppress the spread of rumors on the Internet, prevent cascading failures of power grids, etc. However, our ability to accurately locate the diffusion source is strictly limited by incomplete information of nodes and inevitable randomness of diffusion process. In this paper, we propose an efficient optimization approach via maximum likelihood estimation to locate the diffusion source in complex networks with limited observations. By modeling the informed times of the observers, we derive an optimal source localization solution for arbitrary trees and then extend it to general graphs via proper approximations. The numerical analyses on synthetic networks and real networks all indicate that our method is superior to several benchmark methods in terms of the average localization accuracy, high-precision localization and approximate area localization. In addition, low computational cost enables our method to be widely applied for the source localization problem in large-scale networks. We believe that our work can provide valuable insights on the interplay between information diffusion and source localization in complex networks.
Highlights
Diffusion dynamics taking places on complex networks has been a long-term hot topic with importance value to help us better understand many ubiquitous natural phenomena and social behaviors
We present the Gaussian-based localization and deduction (GLAD) as a simple and efficient framework for locating the diffusion source in networks based on partial timestamps
Since our optimizations were based on the assumption of Gaussian distribution on edge delays, and the diffusion parameters were deduced from observers, we named our method Gaussian-based localization and deduction (GLAD)
Summary
Diffusion dynamics taking places on complex networks has been a long-term hot topic with importance value to help us better understand many ubiquitous natural phenomena and social behaviors. If decision-makers, such as managers and politicians, can identify the diffusion sources as early as possible, they are more likely to make the right decision in time and avoid economic losses and social panic due to the associated tragedies. In order to better resist the potential terrible consequences induced by those diffusion processes, there is a great need for us to develop efficient strategies to locate the source of diffusion and devise control methods as early as possible. The exact number of sources and the zero time at which diffusion first occurred are usually unknown to us. Even if we can get full access to such information, the
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