A survey on influence spreader identification in online social network

Kumaran, P.; Chitrakala, S.

doi:10.1109/icices.2016.7518924

Cited by 5 publications

(6 citation statements)

References 12 publications

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“…These parameters include the tie-strength, homophily, communities, opinion, user roles, and topics. Kumaran et al [15] compare the methods, algorithms, and techniques for influence spreader detection. Their research view is related to influence diffusion.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Information Diffusion in Online Social Networks: Models and Methods

et al. 2017

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By now, personal life has been invaded by online social networks (OSNs) everywhere. They intend to move more and more offline lives to online social networks. Therefore, online social networks can reflect the structure of offline human society. A piece of information can be exchanged or diffused between individuals in social networks. From this diffusion process, lots of latent information can be mined. It can be used for market predicting, rumor controlling, and opinion monitoring among other things. However, the research of these applications depends on the diffusion models and methods. For this reason, we survey various information diffusion models from recent decades. From a research process view, we divide the diffusion models into two categories-explanatory models and predictive models-in which the former includes epidemics and influence models and the latter includes independent cascade, linear threshold, and game theory models. The purpose of this paper is to investigate the research methods and techniques, and compare them according to the above categories. The whole research structure of the information diffusion models based on our view is given. There is a discussion at the end of each section, detailing related models that are mentioned in the literature. We conclude that these two models are not independent, they always complement each other. Finally, the issues of the social networks research are discussed and summarized, and directions for future study are proposed.

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Section: Introductionmentioning

confidence: 99%

A Survey on Information Diffusion in Online Social Networks: Models and Methods

et al. 2017

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“…The linear threshold model was first suggested to define collective behavior [1] and was implemented in economics and sociology to describe a sequence of binary decision events.…”

Section: Linear Threshold Modelmentioning

confidence: 99%

“…Some researchers in the field of information diffusion have suggested several methods. [1] Compare the methods, algorithms, or techniques of spreader detection. [2] Talk about the information diffusion parameters.…”

Section: Introductionmentioning

confidence: 99%

A Survey on Information Diffusion in Online Social Networks

Yang

2020

Proceedings of the 2020 European Symposium on Software Engineering

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“…Besides the above static features, the dynamic issues, such as network evolution, information diffusion, and cascading failure, can help researchers to better explore the rules behind social networks. In recent years, the problem of identifying influential nodes has attracted wide attention [41][42][43]. The influence of a node is usually reflected by the ability of spreading information.…”

Section: Related Workmentioning

confidence: 99%

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

Mao

Xiao

2018

Complexity

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In the era of big data, social network has become an important reflection of human communications and interactions on the Internet. Identifying the influential spreaders in networks plays a crucial role in various areas, such as disease outbreak, virus propagation, and public opinion controlling. Based on the three basic centrality measures, a comprehensive algorithm named PARW-Rank for evaluating node influences has been proposed by applying preference relation analysis and random walk technique. For each basic measure, the preference relation between every node pair in a network is analyzed to construct the partial preference graph (PPG). Then, the comprehensive preference graph (CPG) is generated by combining the preference relations with respect to three basic measures. Finally, the ranking of nodes is determined by conducting random walk on the CPG. Furthermore, five public social networks are used for comparative analysis. The experimental results show that our PARW-Rank algorithm can achieve the higher precision and better stability than the existing methods with a single centrality measure.

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A survey on influence spreader identification in online social network

Cited by 5 publications

References 12 publications

A Survey on Information Diffusion in Online Social Networks: Models and Methods

A Survey on Information Diffusion in Online Social Networks: Models and Methods

A Survey on Information Diffusion in Online Social Networks

A Comprehensive Algorithm for Evaluating Node Influences in Social Networks Based on Preference Analysis and Random Walk

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