A Hurst-based Diffusion Model using Time Series Characteristics for
Influence Maximization in Social Networks
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.