Threshold Estimation Models for Linear Threshold-Based Influential User Mining in Social Networks

Ashis Talukder,Gwan Hoon Park,Md Golam Rabiul Alam,Choong Seon Hong,Dusit Niyato,Nguyen H Tran

doi:10.1109/access.2019.2931925

Abstract

Influence Maximization (IM) is a popular social network mining mechanism that mines influential users for viral marketing in social networks. Most of the Influence Maximization techniques employ either the independent cascade (IC) or linear threshold (LT) model in the node activation process. In the IC model, all the active in-neighbors are given a single chance to activate a node with a particular probability whereas, in the LT model, a node is activated if the aggregated influence of all the activated in-neighbors is no less than a threshold value. Thus, the threshold plays a significant role in the LT-based influence maximization. In this paper, we comprehensively survey the different threshold values used in various IM models. Based on the survey, we observe that the current studies lack threshold estimation models. Therefore, we develop a system model and propose four threshold estimation models based on influence-weight and degree distribution. The empirical results show that our algorithms generate threshold values that resemble the thresholds used by most IM algorithms along with faster running time. Besides, the proposed models are scalable and applicable to any influence-weight estimation technique and offer narrower threshold ranges rather than the broad ranges used in many existing works.

Highlights

The Social Network-based viral marketing research has been incredibly popular in the last one and a half decades
A certain level of influence-weight can be assigned as a threshold value, which is the main intuition of our proposed models
In this paper, we conduct an extensive survey on the different threshold values used in various Influence Maximization algorithms under the Linear Threshold (LT) model to mine influential users for viral marketing

Summary

Introduction

The Social Network-based viral marketing research has been incredibly popular in the last one and a half decades. The Influence Maximization (IM) problem in a social network has become an essential and potential research direction in this field. The IM problem mines a small number of seed users in such a way that the total number of nodes activated by those seed users is maximized if the seed users are initially activated. The seminal work in this field was conducted by. The associate editor coordinating the review of this manuscript and approving it for publication was Haishuai Wang. Kempe et al [1] in 2003. The authors have formulated two classical models to solve the IM problem, e.g., the Independent Cascade (IC) and Linear Threshold (LT) models

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