Influential users identification under the non-progressive LTIRS model

Identification of the key influencers is one of the most important strategies for initiating any transmission process in a social network. However, many of the current studies on influence transmission concentrate primarily on the progressive dissemination phenomenon. Furthermore, the various methods for selecting key influencers that are being explored so far either depend on the structure of the network or the connections between users. As a result, the topology of the network may affect the functioning of these existing methods. Therefore, this paper presents an LTIRS non-progressive model that works on comprehending the non-progressive influence transmission phenomenon, where an influencer may eventually lose their power to influence for a period and the same influencer may revive and engage in active transmission again in the future. In this work, we also propose a scheme named the “AASN” method where the queueing theory is applied in the LTIRS model to investigate the impact of change in the state of a node and compute its influence capacity. Thus, this method provides an efficient way for selecting the key influencer irrespective of the topological properties of the network. The effectiveness of the proposed method is demonstrated by experimental studies on a few real-world datasets.