Online social networks have grown exponentially in the recent years while finding applications in real life like marketing, recommendation systems, and social awareness campaigns. An important research area in this field is Influence Maximization, which pertains to finding methods for maximizing the spread of information (influence) across an OSN. Existing works in IM widely use a pre-defined edge propagation probability for node activation. Hurst exponent (H), which depicts the self-similarity in the time series depicting a user's past interaction behaviour, has also been used as activation criteria. In this work, we propose a Time Series Characteristic based Hurstbased Diffusion Model (TSC-HDM), which calculates H based on the stationary or non-stationary characteristic of the time series. TSC-HDM selects a handful of seed nodes and activates a seed node's inactive successor only if H > 0.5. The proposed model has been tested on four real-world OSN datasets. The results have been compared against four other IM models -Independent Cascade, Weighted Cascade, Trivalency, and Hurst-based Influence Maximization. TSC-HDM is found to have achieved as much as 590% higher expected influence spread as compared to the other models. Moreover, TSC-HDM has attained 344% better average influence spread than other state-of-the-art models namely LIR, A-Greedy, LPIMA, Genetic
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.