A survey on information diffusion and competitive influence maximization in social networks

被引:0
作者
Solanki, Shano [1 ]
Kumar, Mukesh [2 ]
Kumar, Rakesh [3 ]
机构
[1] NITTTR, Dept Comp Sci & Engn, Chandigarh, India
[2] Panjab Univ, Dept Comp Sci & Engn, UIET, Chandigarh, India
[3] Cent Univ Haryana, Dept Comp Sci & Engn, Mahendergarh, India
关键词
Social networks; Information diffusion; Influence maximization; Competitive influence maximization; Information diffusion models; Competitive influence maximization algorithms; NODES;
D O I
10.1007/s13278-025-01459-2
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Social Networks consist of nodes representing users and edges representing interaction among them. They are widely used platforms for information diffusion among various nodes, and influential nodes play a vital role in quickly spreading messages. In viral marketing, information diffusion means to spread information about products, events, and many more to achieve maximum information spread. One of the challenges of information diffusion on social networks is influence maximization, which deals with identifying an optimal number of nodes whose collective influence spread in a social network is maximum. Researchers studied it at an overall network or a community level and as a Competitive Influence Maximization problem where more than one competitor is involved in the information diffusion process. The main highlights of this survey paper are related to various concepts required to be understood in the field of information diffusion and its application in Competitive Influence Maximization. This survey paper focuses on information diffusion and its applications, major factors affecting it, and research challenges in this domain. It also covers the essential concepts of influence maximization and its different types. In addition, variants of competitive influence maximization studied by various researchers in the past two decades are also given. The survey paper also covers various information diffusion models and their extended versions for competitive scenarios, datasets used, and challenges ahead in the Competitive Influence Maximization research domain.
引用
收藏
页数:26
相关论文
共 75 条
[1]   HWSMCB: A community-based hybrid approach for identifying influential nodes in the social network [J].
Ahmad, Amreen ;
Ahmad, Tanvir ;
Bhatt, Abhishek .
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2020, 545
[2]  
Alavi M, 2021, LBCIM: loyalty based competitive influence maximization with? -greedy MCTS strategy, P1
[3]  
Alkhamees N, 2016, 2016 IEEE INTERNATIONAL CONFERENCE ON BIG DATA (BIG DATA), P1670, DOI 10.1109/BigData.2016.7840781
[4]  
Ansari A, 2019, arXiv, P1
[5]   Analysis of Political Sentiment Orientations on Twitter [J].
Ansari, Mohd Zeeshan ;
Aziz, M. B. ;
Siddiqui, M. O. ;
Mehra, H. ;
Singh, K. P. .
INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND DATA SCIENCE, 2020, 167 :1821-1828
[6]  
Arrami S, 2017, Intelligent interactive multimedia systems and services, V76, P362, DOI [10.1007/978-3-319-59480-4, DOI 10.1007/978-3-319-59480-4]
[7]   Opinion leader detection: A methodological review [J].
Bamakan, Seyed Mojtaba Hosseini ;
Nurgaliev, Ildar ;
Qu, Qiang .
EXPERT SYSTEMS WITH APPLICATIONS, 2019, 115 :200-222
[8]   A survey on influence maximization in a social network [J].
Banerjee, Suman ;
Jenamani, Mamata ;
Pratihar, Dilip Kumar .
KNOWLEDGE AND INFORMATION SYSTEMS, 2020, 62 (09) :3417-3455
[9]  
Bharathi Shishir., 2007, Competitive influence maximization in social networks, P306
[10]   Identifying Top-k Nodes in Social Networks: A Survey [J].
Bian, Ranran ;
Koh, Yun Sing ;
Dobbie, Gillian ;
Divoli, Anna .
ACM COMPUTING SURVEYS, 2019, 52 (01)