ABSTRACT
The topic of source identification has attracted wide attention from researchers. In practice, the source identification method aims to locate the sources of rumors, computer viruses, and epidemics, such as COVID-19. However, there are two main problems with existing propagation source detection methods. First, most source detection methods are based on infinite networks, not in line with reality. Second, sources are often randomly selected in simulations, but different sources often cause significantly different detection results in real-world applications. To this end, we study how does the source location impact the effectiveness of source detection in finite networks. This paper first proposes a diameter-based node division method to classify the nodes based on their structural location. We further offer different evaluation indicators to measure the effectiveness of source detection methods. Then, we conduct systematic experiments on three synthetic networks and two real-world networks. Our experiments demonstrate that the location of the source directly effects detection effectiveness in finite networks for all source detection methods. Specifically, sources closer to the network boundary will lead to worse detection performance. It means that attackers can choose sources close to the network boundary to reduce the probability of detection to achieve a larger spreading scale. © 2022 IEEE.