Multiple rumor source identification in social networks leveraging community and monitor information

Multiple rumor source identification (MRSI) in social networks has become a challenging problem for controlling rumors from spreading automatically. Even though several techniques have been introduced for MRSI, most of them were introduced based on the fact that they knew the underlying diffusion model in advance, which is mostly not possible in real-world scenarios. So, this paper proposes a new algorithm called Multi-Source Detection, which uses Community and Monitor information (MSDCM). Our algorithm has two stages: detecting multiple rumor sources using the community and monitor information. First, identify the communities in the network to find the suspicious communities with the possible sources using the monitors' information available in those communities. Next, detect a single source in each of the most likely suspicious communities by back-tracking from each monitor using edge weights in that community. Experimented on several datasets, including small-scale, large-scale, and artificial networks. Our results demonstrate that the designed algorithm is more efficient than the state-of-the-art algorithms.