Effects of Different Recommendation Algorithms on Structure of Social Networks
Abstract
In this paper, the effects of two algorithms (i.e., K-means clustering, Cosine similarity) on the structure of a social network (i.e., Twitter, scientific research papers) is studied to examine the formation of communities when the users follow the recommendations provided by a simulator. The relationship among the users can be either follower-followee in a Twitter dataset or a paper-publication venue in a scientific research paper dataset. The purpose is to evaluate how detected communities and the resulted network graph differ when following the recommendations provided by the employed algorithms after the system recommends the top-N recommendations for a selected user.
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Link recommendation algorithms have the potential to shape the overall evolution of social networks.
“Link recommendation algorithms have the potential to shape the overall evolution of social networks 120-122 .”
From Machine culture (2023) · cites this paper as [120]
Cites
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Brinkmann, L., Baumann, F., Bonnefon, J.-F., Derex, M., Müller, T. F., Nussberger, A.-M., Czaplicka, A., Acerbi, A., Griffiths, T. L., Henrich, J., et al. (2023) · Nature Human Behaviour · doi:10.1038/s41562-023-01742-2 · Cited by 132 · see citing works
- Authors
- Banihashemi, S., & Abhari, A.
- Affiliations
- Toronto Metropolitan University (CA)
- ORCID
- Banihashemi, Sepideh
- Venue
- 2021 International Conference on Computational Science and Computational Intelligence (CSCI)
- Publisher
- IEEE
- Published
- 2021-12
- Pages
- 1395-1400
- OpenAlex
- W4285079561
- Cited by
- 1
- References
- 17
- Citations by year
- 2023: 1
- FWCI
- 0.38
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Computational Science and Computational Intelligence (CSCI) (publishedVersion)
- Topics
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Physical Sciences › Computer Science › Information Systems › Information Retrieval and Data Mining
- Keywords
- recommendation algorithms, social network structure, community detection, K-means clustering, cosine similarity, Twitter, publication venues, top-N recommendation, network graphs
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