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.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Moneypenny NF, Flinn SD. Influence-based social network advertising. U.S. Patent Application No. 12/172,236
Leskovec J, Singh A, Kleinberg J (2006) Patterns of influence in a recommendation network. In: Pacific-Asia conference on knowledge discovery and data mining. Springer, Berlin, Heidelberg
Carneiro A (2000) How does knowledge management influence innovation and competitiveness? J Knowl Manag 4(2):87–98
Kempe D, Kleinberg J, Tardos É (2005) Influential nodes in a diffusion model for social networks. In: International colloquium on automata, languages, and programming. Springer, Berlin, Heidelberg
Al-Zaidy R et al. Mining criminal networks from unstructured text documents. Digit Investig 8(3):147–160 (2012)
Puigbo J-Y et al (2014) Influencer detection approaches in social networks: a current state-of-the-art. CCIA
Li Y et al (2013) Influence diffusion dynamics and influence maximization in social networks with friend and foe relationships. In: Proceedings of the sixth ACM international conference on web search and data mining. ACM
Rosenthal S, Mckeown K (2017) Detecting influencers in multiple online genres. ACM Trans Internet Technol (TOIT) 17(2):12
Trusov M, Bodapati AV, Bucklin RE (2010) Determining influential users in internet social networks. J Mark Res 47(4):643–658
Fang Q et al (2004) Topic-sensitive influencer mining in interest-based social media networks via hypergraph learning. IEEE Trans Multimed 16(3):796–812
Subbian K, Aggarwal CC, Srivastava J (2016) Querying and tracking influencers in social streams. In: Proceedings of the ninth ACM international conference on web search and data mining. ACM
Gomez Rodriguez M, Leskovec J, Schölkopf B (2013) Structure and dynamics of information pathways in online media. In: Proceedings of the sixth ACM international conference on web search and data mining. ACM
Uddin S, Hossain L, Rasmussen K (2013) Network effects on scientific collaborations. PLoS ONE 8(2):e57546
Wallace ML, Larivière V, Gingras Y (2012) A small world of citations? The influence of collaboration networks on citation practices. PLoS ONE 7(3):e33339
Belkin M, Niyogi P (2003) Laplacian eigenmaps for dimensionality reduction and data representation. Neural Comput 15(6):1373–1396
Sun J, Ovsjanikov M, Guibas L (2009) A concise and provably informative multi‐scale signature based on heat diffusion. In: Computer graphics forum, vol 28, no 5. Blackwell Publishing Ltd
Ma H et al (2008) Mining social networks using heat diffusion processes for marketing candidates selection. In: Proceedings of the 17th ACM conference on information and knowledge management. ACM
Box GEP, Cox DR (1964) An analysis of transformations. J R Stat Soc Ser B (Methodol) 211–252
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-10-8240-5_29
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-8239-9
Online ISBN: 978-981-10-8240-5
eBook Packages: EngineeringEngineering (R0)