Source detection in social networks under independent cascade model

With the popularization of social networks, the source detection problem has attracted a lot of attention since rumors can spread widely in social networks in a short time. Some existing heuristic methods tend to choose the most influential user as the source, which may result in inaccurate results. Besides, some solutions based on maximum likelihood estimation (MLE) are also proposed, where the key issue is to quantify the probability of a source activating a nonadjacent user. Although Monte Carlo method can be used to estimate this probability, it is extremely time-consuming for large-scale networks. To address this problem, we adopt the duplicate forwarding model to analyze the diffusion process in social networks, which is close to the independent cascade model. Then we calculate the probability that a user receives at least one message after a source generates a message, and use it to detect the source by adopting MLE. Besides, to make research cases more reasonable, we consider snapshots where at least two active users are observed after the diffusion process terminates. Then we need to adjust the likelihood estimation to get better results. Experimental results demonstrate that our method not only achieves better accuracy but also consumes less time than referenced methods. We believe the method proposed here offers valuable insights to solve the source detection problem in large-scale networks.