Discovering Influencers for Spreading in Weighted Networks

Identifying the influential nodes in networks is an important issue for efficient information diffusion, controlling rumors and diseases and optimal use of network structure. the degree centrality which considers local topology features, does not produce very reliable results. despite better results of global centrality such as betweenness centrality and closeness centrality, they have high computational complexity. so, we propose semi-local centrality measure to identify influential nodes in weighted networks by considering node degree, edges weight and neighboring nodes. this method runs in o(n(k)^2) .so, it is feasible for large scale network. the results of applying the proposed method on weighted networks and comparing it with susceptible-infected-recovered model, show that it performs good and the influential nods are generated by our method can spread information well.