GSI: An Influential Node Detection Approach in Heterogeneous Network Using Covid-19 as Use Case

The growth of COVID-19, caused by the SARS-CoV-2 virus, has turned into an unprecedented pandemic in the last century. It is crucial to identify superspreading nodes to prevent the pandemic's progress. Most available superspreader identification techniques consider only a single or few network metrics related to the complex network's topological structure. Furthermore, it is more challenging to determine influential spreaders from heterogeneous structures of networks. In a disease transmission network, the degree of heterogeneity is essential to locate the path of the infection spread. Therefore, it is required to have an extended degree of centrality to collect information from various neighborhood levels. This article presents an approach, namely, global structure influence (GSI), which considers network nodes' local and global influence. This method can gather information from multiple levels of the neighborhood. Evaluation of our proposed method is done by considering different types of networks, i.e., social networks, highly heterogeneous human contact networks, and epidemiological networks, and also by using the benchmark susceptible-infected-recovered (SIR) epidemic model. The GSI technique provides real- spreading dynamics across various network structures and has outperformed the baseline techniques with an average Kendall's tau improvement range from 0.017 to 0.278. This study will help to identify the superspeaders in real applications, where pathogens spread quickly because of close contact, such as the recently witnessed COVID-19 pandemic.