MODM: Multi-objective diffusion model for dynamic social networks using evolutionary algorithm

Iram Fatima, Muhammad Fahim, Young Koo Lee*, Sungyoung Lee

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


A lot of research efforts have been made to model the diffusion process in social networks that varies from adoption of products in marketing strategies to disease and virus spread. Previously, a diffusion process is usually considered as a single-objective optimization problem, in which different heuristics or approximate algorithms are applied to optimize an objective of spreading single piece of information that captures the notion of diffusion. However, in real social networks individuals simultaneously receive several pieces of information during their communication. Single-objective solutions are inadequate for collective spread of several information pieces. Therefore, in this paper, we propose a Multi-Objective Diffusion Model (MODM) that allows the modeling of complex and nonlinear phenomena of multiple types of information exchange, and calculate the information worth of each individual from different aspects of information spread such as score, influence and diversity. We design evolutionary algorithm to achieve the multi-objectives in single diffusion process. Through extensive experiments on a real world data set, we have observed that MODM leads to a richer and more realistic class of diffusion model compared to a single objective. This signifies the correlation between the importance of each individual and his information processing capability. Our results indicate that some individuals in the network are naturally and significantly better connected in terms of receiving information irrespective of the starting position of the diffusion process.

Original languageEnglish
Pages (from-to)738-759
Number of pages22
JournalJournal of Supercomputing
Issue number2
Publication statusPublished - Nov 2013
Externally publishedYes

Bibliographical note

Funding Information:
Acknowledgements This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 2012047478).

Copyright 2021 Elsevier B.V., All rights reserved.


  • Dynamic social networks
  • Evolutionary algorithm
  • Information diffusion
  • Multi-objective optimization

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Theoretical Computer Science
  • Hardware and Architecture


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