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Evolving social network analysis: A case study on mobile phone data

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Evolving social network analysis: A case study on mobile phone data. / Dutta Baruah, Rashmi; Angelov, Plamen.
Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on. IEEE, 2012. p. 114-120.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Dutta Baruah, R & Angelov, P 2012, Evolving social network analysis: A case study on mobile phone data. in Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on. IEEE, pp. 114-120. https://doi.org/10.1109/EAIS.2012.6232815

APA

Dutta Baruah, R., & Angelov, P. (2012). Evolving social network analysis: A case study on mobile phone data. In Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on (pp. 114-120). IEEE. https://doi.org/10.1109/EAIS.2012.6232815

Vancouver

Dutta Baruah R, Angelov P. Evolving social network analysis: A case study on mobile phone data. In Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on. IEEE. 2012. p. 114-120 doi: 10.1109/EAIS.2012.6232815

Author

Dutta Baruah, Rashmi ; Angelov, Plamen. / Evolving social network analysis: A case study on mobile phone data. Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on. IEEE, 2012. pp. 114-120

Bibtex

@inproceedings{bee55f346afd4487a1d552a790a3c464,
title = "Evolving social network analysis: A case study on mobile phone data",
abstract = "Mobile phone data can provide rich information on human activities and their social relationships which are dynamic in nature. Analysis of such social networks emerging from phone calls of mobile users can be useful in many aspects. In this paper we report the methods and results from a case study on the analysis of a social network from mobile phone data. The analysis involves tracking the dynamics of the network, identifying key individuals and their close associates,and identifying individuals having communication pattern similar to the key individuals. We introduce novel measures to quantify, the evolution in the network, significance of an individual, and social association of an individual. In order togroup individuals having similar communication pattern, we applied recently proposed online clustering approach called eClustering (evolving clustering) due to its adaptive nature and low computational overhead. The results show the pertinence of the proposed quantification measures to analysis of evolving social network.",
keywords = "Evolving social network , dynamic social network , evolving clustering , online clustering , social network analysis",
author = "{Dutta Baruah}, Rashmi and Plamen Angelov",
year = "2012",
doi = "10.1109/EAIS.2012.6232815",
language = "English",
isbn = "978-1-4673-1728-3",
pages = "114--120",
booktitle = "Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on",
publisher = "IEEE",

}

RIS

TY - GEN

T1 - Evolving social network analysis: A case study on mobile phone data

AU - Dutta Baruah, Rashmi

AU - Angelov, Plamen

PY - 2012

Y1 - 2012

N2 - Mobile phone data can provide rich information on human activities and their social relationships which are dynamic in nature. Analysis of such social networks emerging from phone calls of mobile users can be useful in many aspects. In this paper we report the methods and results from a case study on the analysis of a social network from mobile phone data. The analysis involves tracking the dynamics of the network, identifying key individuals and their close associates,and identifying individuals having communication pattern similar to the key individuals. We introduce novel measures to quantify, the evolution in the network, significance of an individual, and social association of an individual. In order togroup individuals having similar communication pattern, we applied recently proposed online clustering approach called eClustering (evolving clustering) due to its adaptive nature and low computational overhead. The results show the pertinence of the proposed quantification measures to analysis of evolving social network.

AB - Mobile phone data can provide rich information on human activities and their social relationships which are dynamic in nature. Analysis of such social networks emerging from phone calls of mobile users can be useful in many aspects. In this paper we report the methods and results from a case study on the analysis of a social network from mobile phone data. The analysis involves tracking the dynamics of the network, identifying key individuals and their close associates,and identifying individuals having communication pattern similar to the key individuals. We introduce novel measures to quantify, the evolution in the network, significance of an individual, and social association of an individual. In order togroup individuals having similar communication pattern, we applied recently proposed online clustering approach called eClustering (evolving clustering) due to its adaptive nature and low computational overhead. The results show the pertinence of the proposed quantification measures to analysis of evolving social network.

KW - Evolving social network

KW - dynamic social network

KW - evolving clustering

KW - online clustering

KW - social network analysis

U2 - 10.1109/EAIS.2012.6232815

DO - 10.1109/EAIS.2012.6232815

M3 - Conference contribution/Paper

SN - 978-1-4673-1728-3

SP - 114

EP - 120

BT - Evolving and Adaptive Intelligent Systems (EAIS), 2012 IEEE Conference on

PB - IEEE

ER -