Identifying Network Propagation Sources Using Advanced Centrality Measures

We live in a time dominated by interconnected networks surrounding us on all fronts. The emergence of social media platforms has driven the expansion of social networks, facilitating fast communication worldwide. Responses to content shared on these platforms can be seen as a propagation process, where information spreads through social networks. Analyzing propagation graphs presents a significant challenge in identifying sources, which is crucial in various fields. This includes detecting the origins of disinformation, identifying patient zero in an epidemic, and tracing the initial sources of viral trends or malware. Numerous studies have attempted to identify these sources using methods similar to centrality measures which assign a value indicating the likelihood of being a source. While centrality measures are a popular topic, with many new measures introduced each year, only a few have been explored in the context of source identification. This article explores a wide range of centrality measures in the context of source identification. The results help identify the most effective measures and pave the way for the development of more efficient detection techniques. Additionally, an analysis was conducted considering multiple hops in the propagation network, providing deeper insights into the impact of extended neighborhood structures on detection performance.