A robust optimization model for influence maximization in social networks with heterogeneous nodes

Reza Ghesmati,Mir Saman Pishvaee,Mehrdad Agha Mohammad Ali Kermani

doi:10.1186/s40649-021-00096-x

Abstract

Influence maximization is the problem of trying to maximize the number of influenced nodes by selecting optimal seed nodes, given that influencing these nodes is costly. Due to the probabilistic nature of the problem, existing approaches deal with the concept of the expected number of nodes. In the current research, a scenario-based robust optimization approach is taken to finding the most influential nodes. The proposed robust optimization model maximizes the number of infected nodes in the last step of diffusion while minimizing the number of seed nodes. Nodes, however, are treated as heterogeneous with regard to their propensity to pass messages along; or as having varying activation thresholds. Experiments are performed on a real text-messaging social network. The model developed here significantly outperforms some of the well-known existing heuristic approaches which are proposed in previous works.

Highlights

People often learn from each other, and this has important implications for such diverse things as how they find employment, what movies they see, which products they purchase, how technology becomes adopted, whether or not they participate in government programs or social events, and whether they protest [1,2,3]
The desired solution can be determined by the social agent by making a trade-off between the two objectives, which are the number of seed nodes and the resulting costs and the expected number of final infected nodes
Influence maximization is the problem of finding most influential nodes in a network to maximize the spread of influence

Summary

Introduction

People often learn from each other, and this has important implications for such diverse things as how they find employment, what movies they see, which products they purchase, how technology becomes adopted, whether or not they participate in government programs or social events, and whether they protest [1,2,3]. Another research that considered the combinatorial optimization problem of finding most influential nodes in social networks is [31] They proposed a method of efficiently estimating the number of influenced nodes at termination based on bond percolation and graph theory; and, they provide a practical solution do the influence maximization problem on G = (V , E) under the greedy hill-climbing algorithm. It seems that the study which done by He and Kempe [45] is first work that tries to address the issue of uncertainty of parameter estimates impacting the influence maximization tasks They investigated the problem from algorithmic point of view and did not proposed any robust or non-robust mathematical programming model. Since ε can hold integer numbers, its intuitive interpretation is the number of seed nodes

Methods

Findings

Conclusions