loading page

A Hurst-based Diffusion Model using Time Series Characteristics for Influence Maximization in Social Networks
  • +3
  • Vinay Chamola,
  • Bhawna Saxena,
  • Vikas Saxena,
  • Nishit Anand,
  • Vikas Hassija,
  • Amir Hussain
Vinay Chamola
Birla Institute of Technology & Science Pilani

Corresponding Author:vinay.chamola@pilani.bits-pilani.ac.in

Author Profile
Bhawna Saxena
Jaypee Institute of Information Technology
Author Profile
Vikas Saxena
Jaypee Institute of Information Technology
Author Profile
Nishit Anand
Jaypee Institute of Information Technology
Author Profile
Vikas Hassija
Kalinga Institute of Industrial Technology
Author Profile
Amir Hussain
Edinburgh Napier University School of Computing
Author Profile


Online Social Networks (OSNs) have grown exponentially in the last few years due to their applications in real life like marketing, recommendation systems, and social awareness campaigns. One of the most important research areas in this field is Influence Maximization (IM). IM pertains to finding methods to maximize the spread of information (or influence) across a social network. Previous works in IM have focused on using a pre-defined edge propagation probability or using the Hurst exponent (H) to identify which nodes to be activated. This is calculated on the basis of self-similarity in the time series depicting a user’s (node) past temporal interaction behaviour. In this work, we propose a Time Series Characteristic based Hurst-based Diffusion Model (TSC-HDM). The model calculates Hurst Exponent (H) based on the stationary or non-stationary characteristic of the time series. Furthermore, our model selects a handful of seed nodes and activates every seed node’s inactive successor only if H>0.5 . The process is continued until the activation of successor nodes is not possible. The proposed model was tested on 4 datasets - UC Irvine messages, Email EU-Core, Math Overflow 3, and Linux Kernel mailing list. We have also compared the results against 4 other Influence Maximisation models - Independent Cascade (IC), Weighted Cascade (WC), Trivalency (TV), and Hurst-based Influence Maximisation (HBIM). Our model achieves as much as 590% higher expected influence spread as compared to the other models. Moreover, our model attained 344% better average influence spread than other state-of-the-art models.
24 Feb 2023Submitted to Expert Systems
24 Feb 2023Submission Checks Completed
24 Feb 2023Assigned to Editor
06 Mar 2023Reviewer(s) Assigned
29 Mar 2023Review(s) Completed, Editorial Evaluation Pending
19 Apr 2023Editorial Decision: Revise Minor
28 Apr 20231st Revision Received
28 Apr 2023Submission Checks Completed
28 Apr 2023Assigned to Editor
01 May 2023Reviewer(s) Assigned
22 May 2023Review(s) Completed, Editorial Evaluation Pending
24 May 2023Editorial Decision: Accept