A multi-objective linear threshold influence spread model solved by swarm intelligence-based methods

The influence maximization problem (IMP) is one of the most important topics in social network analysis. It consists of finding the smallest seed of users that maximizes the influence spread in a social network. The main influence spread models are the linear threshold model (LT-model) and the independent cascade model (IC-model). These models have mainly been treated by using the singleobjective paradigm which covers just one perspective: maximize the influence spread starting by given seed size, or minimize the seed set to reach a given number of influenced nodes. Sometimes, this minimization problem has been called the least cost influence problem (LCI). In this work, we propose a new optimization model for both perspectives under conflict, through the LT-model, by applying a binary multi-objective approach. Swarm intelligence methods are implemented to solve our proposal on real networks. Results are promising and suggest that the new multi-objective solution proposed can be properly solved in harder instances. (C) 2020 Elsevier B.V. All rights reserved.Y

Automated summary

A new optimization model was proposed to address both perspectives under conflict through the LT-model using a binary multi-objective approach, and swarm intelligence methods were implemented to solve the proposal on real networks. The results are promising, suggesting that the new multi-objective solution can be effectively solved even in harder instances.