IMDCS:influence maximization with type-diversity by leveraging community structure-Reference-Cited by-同舟云学术

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IMDCS:influence maximization with type-diversity by leveraging community structure

Published:2022-12-09 Issue: Volume: Page:
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Wang Xiaojie^ORCID,Slamu Wushour,Kadeer Abudureheman,Wang Sixiu,Hou Xiaojing

Funder

Natural Science Foundation of Xinjiang

Key Technologies Research and Development Program

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Numerical Analysis,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00607-022-01134-w.pdf

Reference47 articles.

1. Domingos P, Richardson M (2001) Mining the network value of customers. Association for Computing Machinery, New York, USA, pp 57–66

2. Saleem MA, Kumar R, Calders T, Xie X, Pedersen TB (2017) Location influence in location-based social networks. In: Proceedings of the tenth ACM international conference on web search and data mining WSDM ’17. Association for Computing Machinery, New York, USA, pp 621–630

3. Chen X, Deng L, Zhao Y, Zhou X, Zheng K (2021) Community-based influence maximization in location-based social network. World Wide Web 24(6):1903–1928

4. Li W, Zhong K, Wang J, Chen D (2021) A dynamic algorithm based on cohesive entropy for influence maximization in social networks. Expert Syst Appl 169:114207

5. Rui X, Yang X, Fan J, Wang Z (2020) A neighbour scale fixed approach for influence maximization in social networks. Computing 102(2):427–449

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