2024/03/22 by Pei-Duo Yu, Yu, Pei-Duo, Chee Wei Tan +1 · 1 citation
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Markov Chains and Monte Carlo Methods #Social and Information Networks (cs.SI) #Stochastic processes and statistical mechanics
paper · pdf · doi:10.48550/arxiv.2403.14890
openalex publication_date 2024/03/22 · openalex created_date 2024/03/26 · openalex updated_date 2026/07/28
Identifying the source of epidemic-like spread in networks is crucial for removing internet viruses or finding the source of rumors 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. Unlike previous works that rely heavily on structural properties alone, our method also incorporates temporal data for more precise source detection. We highlight the utility of the Gamma function for analyzing the ratio of the likelihood being the source between nodes asymptotically. Comprehensive evaluations confirm algorithmic effectiveness in diverse network scenarios, advancing source detection in large-scale network analysis and information dissemination strategies.