Identifying influential nodes in complex networks based on ASP theory and centrality criteria

From social and technological to biological, complex networks are found in a lot of domains. Inherently, such systems possess dynamic and intricate characteristics. One of the core problems in network study is the detection of influential nodes that decisively play a major role in the structure and functionality of such networks. This paper proposes a new method for measuring node importance by fusing the GIN of each node and ASP. Not only does it consider the inherent importance of a target node, but it also considers the overall impact of all nodes within the network. The GIN index gives a quantitative means of measuring the global influence of every node. In the paper, the ASP theory is used to assess node efficiency, considering the relative variation in the length of the shortest path over the whole network. In the present work, the authors introduce a novel centrality measure based on the harmonic mean between the GIN and ASP indices. To this end, the SIRM simulation has been conducted in order to test the diffusion of an epidemic in various network structures for evaluating the effectiveness of the proposed method. Extensive experiments on real networks have shown its efficacy and superiority compared with conventional centrality measures.