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Analysis of evolving social network: methods and results from cell phone data set case study

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Analysis of evolving social network: methods and results from cell phone data set case study. / Dutta Baruah, Rashmi; Angelov, Plamen.
In: International Journal of Social Network Mining, Vol. 1, No. 3-4, 2013, p. 254-279.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Dutta Baruah, R & Angelov, P 2013, 'Analysis of evolving social network: methods and results from cell phone data set case study', International Journal of Social Network Mining, vol. 1, no. 3-4, pp. 254-279. https://doi.org/10.1504/IJSNM.2013.059067

APA

Dutta Baruah, R., & Angelov, P. (2013). Analysis of evolving social network: methods and results from cell phone data set case study. International Journal of Social Network Mining, 1(3-4), 254-279. https://doi.org/10.1504/IJSNM.2013.059067

Vancouver

Dutta Baruah R, Angelov P. Analysis of evolving social network: methods and results from cell phone data set case study. International Journal of Social Network Mining. 2013;1(3-4):254-279. doi: 10.1504/IJSNM.2013.059067

Author

Dutta Baruah, Rashmi ; Angelov, Plamen. / Analysis of evolving social network : methods and results from cell phone data set case study. In: International Journal of Social Network Mining. 2013 ; Vol. 1, No. 3-4. pp. 254-279.

Bibtex

@article{df5eb755816e4979bd42e08261513a3c,
title = "Analysis of evolving social network: methods and results from cell phone data set case study",
abstract = "In this paper, we attempt to detect the changes in the structure of anevolving social network. We define a novel measure to quantify the dynamicsof the network and use it to generate a timeline for the network that indicatesthe changes. We consider that any significant change in the network is asa result of occurrence of some event, and thus, identify the day or time stepwhen the event took place. Further, the analysis involves identification ofkey individuals and their close associates that were active on that day. Finally,the case study involves identification of individuals having communicationbehaviour similar to the key individuals. To group such individuals, we usea recently proposed online clustering approach called evolving clustering(eClustering). We demonstrate the efficacy of the proposed quantificationmeasures by conducting several experiments and comparing the results with theground truth.",
keywords = "evolving social networks, dynamic social networks, social network analysis, online clustering, evolving clustering, eClustering, SNA, cell phones, mobile phones, network dynamics, communication behaviour, key individuals",
author = "{Dutta Baruah}, Rashmi and Plamen Angelov",
year = "2013",
doi = "10.1504/IJSNM.2013.059067",
language = "English",
volume = "1",
pages = "254--279",
journal = "International Journal of Social Network Mining",
issn = "1757-8485",
publisher = "Inderscience",
number = "3-4",

}

RIS

TY - JOUR

T1 - Analysis of evolving social network

T2 - methods and results from cell phone data set case study

AU - Dutta Baruah, Rashmi

AU - Angelov, Plamen

PY - 2013

Y1 - 2013

N2 - In this paper, we attempt to detect the changes in the structure of anevolving social network. We define a novel measure to quantify the dynamicsof the network and use it to generate a timeline for the network that indicatesthe changes. We consider that any significant change in the network is asa result of occurrence of some event, and thus, identify the day or time stepwhen the event took place. Further, the analysis involves identification ofkey individuals and their close associates that were active on that day. Finally,the case study involves identification of individuals having communicationbehaviour similar to the key individuals. To group such individuals, we usea recently proposed online clustering approach called evolving clustering(eClustering). We demonstrate the efficacy of the proposed quantificationmeasures by conducting several experiments and comparing the results with theground truth.

AB - In this paper, we attempt to detect the changes in the structure of anevolving social network. We define a novel measure to quantify the dynamicsof the network and use it to generate a timeline for the network that indicatesthe changes. We consider that any significant change in the network is asa result of occurrence of some event, and thus, identify the day or time stepwhen the event took place. Further, the analysis involves identification ofkey individuals and their close associates that were active on that day. Finally,the case study involves identification of individuals having communicationbehaviour similar to the key individuals. To group such individuals, we usea recently proposed online clustering approach called evolving clustering(eClustering). We demonstrate the efficacy of the proposed quantificationmeasures by conducting several experiments and comparing the results with theground truth.

KW - evolving social networks

KW - dynamic social networks

KW - social network analysis

KW - online clustering

KW - evolving clustering

KW - eClustering

KW - SNA

KW - cell phones

KW - mobile phones

KW - network dynamics

KW - communication behaviour

KW - key individuals

U2 - 10.1504/IJSNM.2013.059067

DO - 10.1504/IJSNM.2013.059067

M3 - Journal article

VL - 1

SP - 254

EP - 279

JO - International Journal of Social Network Mining

JF - International Journal of Social Network Mining

SN - 1757-8485

IS - 3-4

ER -