  In recent years, epidemic modeling in complex networks has found many
applications, including modeling of information or gossip spread in online
social networks, modeling of malware spread in communication networks, and the
most recent model of the COVID-19 pandemic. If the information disseminated is
accurate, for example, maximizing its distribution is desirable, whereas if it
is a rumor or a virus, its spread should be minimized. In this context, it is
very important to identify super-spreaders that maximize or minimize
propagation. Lately, studies for detecting super-spreaders have gained
momentum. Most of the studies carried out aim to distinguish the influences of
nodes under a specific propagation model (such as SIR) using network centrality
measures and subsequently, to rank the nodes accordingly. However, in this
study, we developed an algorithm that approximates the expected influence of
nodes under the popular SIR model. By considering the behavior of the SIR model
and only the shortest paths between nodes, the algorithm ranks the nodes
according to this approximated value. Our developed algorithm is named the
Expected Value Estimation (EVE). We compared the performance of EVE, using
different SIR settings on real datasets, with that of many current well-known
centrality measures. The experimental studies demonstrated that the solution
quality (ranking capability) of EVE is superior to that of its competitors.
