A new centrality measure based on the negative and positive effects of clustering coefficient for identifying influential spreaders in complex networks

Identifying the most influential spreaders with the aim of reaching a maximum spreading ability has been a challenging and crucial topic so far. Many centrality measures have been proposed to identify the importance of nodes in spreader detection process. Centrality measures are used to rank the spreading power of nodes. These centralities belong to either local, semi-local, or global category. Local centralities have accuracy problem and global measures need a higher time complexity that are inefficient for large-scale networks. In contrast, semi-local measures are popular methods that have high accuracy and near-linear time complexity. In this paper, we have proposed a new semi-local and free-parameter centrality measure by applying the natural characteristics of complex networks. The proposed centrality can assign higher ranks for structural holes as better spreaders in the network. It uses the positive effects of second-level neighbors' clustering coefficient and negative effects of node's clustering coefficient in defining the importance of nodes. Therefore, the proposed centrality avoids selection of spreaders that are too close to one another. We compare the proposed method with different centrality measures based on Susceptible-Infected-Recovered (SIR) and Susceptible-Infected (SI) models on both artificial and real-world networks. Experiments on both artificial and real networks show that our method has its competitive advantages over the other compared centralities.