Shapley Value and Extremal Optimization for the Network Influence Maximization Problem

The problem of Network Influence Maximization is approached by an Extremal Optimization algorithm called Shapley value Extremal Optimization (SvEO). The influence maximization problem for the independent cascade model is considered as a cooperative game. In this cooperative game players seek to choo...

Full description

Saved in:
Bibliographic Details
Published in2019 21st International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC) pp. 182 - 189
Main Authors Gasko, Noemi, Suciu, Mihai Alexandru, Kepes, Tamas, Lung, Rodica Ioana
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.2019
Subjects
influence maximization
Shapley value
Online AccessGet full text

Cover

More Information
Summary:The problem of Network Influence Maximization is approached by an Extremal Optimization algorithm called Shapley value Extremal Optimization (SvEO). The influence maximization problem for the independent cascade model is considered as a cooperative game. In this cooperative game players seek to choose seeder nodes to maximize the value of the game computed as the size of the influence set of their cascade model by maximizing their average marginal contribution to all possible player coalitions (i.e. subsets of the seeder set). SvEO is compared with other influence maximization algorithms by means of numerical experiments, with promising results. Possible implications of the use of the Shapley value are discussed using a network constructed from highly cited publication data in the field of computer science.
DOI:10.1109/SYNASC49474.2019.00033