Hybrid optimization algorithm for analysis of influence propagation in social network

Influence maximization(IM) is defined as the problem of identifying a node subset in a social network which increases the spread of influence. IM plays a crucial role in social networks by catalyzing the dissemination of influence, resulting in an augmented count of influenced nodes following the propagation process. The existing researches mainly concentrated on increasing the spread of influence, but did not consider the running time of the network. In this manuscript, the salp swarm algorithm (SSA) and bi-adaptive strategy particle swarm optimization (BiAS-PSO) algorithms are integrated and named as SS-BiAS-PSO algorithm to increase the spread of influence based on the IM problem to minimize the running time of the network. The datasets utilized for the research are Ego-Facebook, Epinions, Gowalla, and HepTh, while linear threshold (LT) is utilized as a diffusion method. Then, the proposed SS-BiAS-PSO algorithm is deployed for the analysis of influence propagation. The proposed algorithm reaches a high influence spread of 645, 680, 715, and 750 with less running times respectively for 10, 20, 30, and 40 seed set sizes in Ego-Facebook. The proposed algorithm proves more effective than the existing techniques like traditional SSA and particle swarm optimization (PSO).