Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/103300
Title: Threshold estimation models for linear threshold-based influential user mining in social networks
Authors: Talukder, Ashis
Tran, Nguyen H.
Niyato, Dusit
Park, Gwan Hoon
Hong, Choong Seon
Mohammad Golam Rabiul Alam
Keywords: Engineering::Computer science and engineering
Influence Maximization
Threshold Estimation
Issue Date: 2019
Source: Talukder, A., Mohammad Golam Rabiul Alam, Tran, N. H., Niyato, D., Park, G. H., & Hong, C. S. (2019). Threshold estimation models for linear threshold-based influential user mining in social networks. IEEE Access, 7, 105441-105461. doi:10.1109/ACCESS.2019.2931925
Series/Report no.: IEEE Access
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.
URI: https://hdl.handle.net/10356/103300
http://hdl.handle.net/10220/49965
DOI: http://dx.doi.org/10.1109/ACCESS.2019.2931925
Rights: © 2019 IEEE. This journal is 100% open access, which means that all content is freely available without charge to users or their institutions. All articles accepted after 12 June 2019 are published under a CC BY 4.0 license*, and the author retains copyright. Users are allowed to read, download, copy, distribute, print, search, or link to the full texts of the articles, or use them for any other lawful purpose, as long as proper attribution is given.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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