Influence maximization in large social networks: Heuristics, models and parameters

被引:37
作者
Sumith, N. [1 ,3 ]
Annappa, B. [1 ]
Bhattacharya, Swapan [2 ]
机构
[1] Natl Inst Technol Karnataka, Dept CSE, Surathkal, India
[2] Jadavpur Univ, Dept CSE, Kolkata, India
[3] Alvas Inst Engn & Technol Karnataka, Dept CSE, Mangalore, India
来源
FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE | 2018年 / 89卷
关键词
Social networks; Structure; Models; Influential users; Viral marketing; Algorithms; INFORMATION DIFFUSION; COMPETITIVE INFLUENCE; THRESHOLD MODELS; CENTRALITY; PREDICTION; NODES; TIME;
D O I
10.1016/j.future.2018.07.015
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Online social networks play a major role not only in socio psychological front, but also in the economic aspect. The way social network serves as a platform of information spread, has attracted a wide range of applications at its doorstep. In recent years, lot of efforts are directed to use the phenomenon of vast spread of information, via social networks, in various applications, ranging from poll analysis, product marketing, identifying influential users and so on. One such application that has gained research attention is the influence maximization problem. The influence maximization problem aims to fetch the top influential users in the social networks. The aim of the paper is to provide a comprehensive analysis on the state of art approaches towards identifying influential users. In this review, we discuss various challenges and approaches to identify influential users in online social networks. This review concludes with future research direction, helping researchers to bring possible improvements to the existing body of work. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码:777 / 790
页数:14
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