Abstract
A social network consists of a collection of social entities and interactions among these entities. It performs a crucial role as a platform for spreading various ideas, informations among its members. Influence analysis in social network has always been a fascinating topic in social network analysis due to its various application areas like targeted advertisement, recommendation system, outcome of a campaign, viral marketing, etc. Most of the social networks are dynamic in nature since the state of these networks evolves over time. Majority of earlier research works have been focused on the topics like influencer detection, influence maximization, and influence diffusion in a static network due to the complexity of constantly evolving social network. In this paper, we have proposed a method based on heat-diffusion process to detect influencers in a dynamic social network. The proposed model can also rank them based on the influence he or she has on others. We have applied our proposed method on the evolving co-authorship networks to detect and rank influential persons.
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Acknowledgements
This paper is an outcome of the work carried out for the project titled “Development of some efficient techniques for applications in the field of Business Analytics and Business Intelligence” under “Mobile and Innovative Computing” under the UGC UPE Phase II scheme of Jadavpur University.
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Sarkar, R., Barman, D., Chowdhury, N. (2018). To Detect the Influencers in a Dynamic Co-authorship Network Using Heat-Diffusion Model. In: Bhattacharyya, S., Gandhi, T., Sharma, K., Dutta, P. (eds) Advanced Computational and Communication Paradigms. Lecture Notes in Electrical Engineering, vol 475. Springer, Singapore. https://doi.org/10.1007/978-981-10-8240-5_29
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DOI: https://doi.org/10.1007/978-981-10-8240-5_29
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