  Identifying the source of epidemic-like spread in networks is crucial for
tasks like removing internet viruses or finding the rumor source in online
social networks. The challenge lies in tracing the source from a snapshot
observation of infected nodes. How do we accurately pinpoint the source?
Utilizing snapshot data, we apply a probabilistic approach, focusing on the
graph boundary and the observed time, to detect sources via an effective
maximum likelihood algorithm. A novel starlike tree approximation extends
applicability to general graphs, demonstrating versatility. We highlight the
utility of the Gamma function for analyzing the asymptotic behavior of the
likelihood ratio between nodes. Comprehensive evaluations confirm algorithmic
effectiveness in diverse network scenarios, advancing rumor source detection in
large-scale network analysis and information dissemination strategies.
