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Testing the hypothesis of preferential attachment in social network formation

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Testing the hypothesis of preferential attachment in social network formation. / House, Thomas; Read, Jonathan M.; Danon, Leon et al.
In: EPJ Data Science, Vol. 4, No. 1, 13, 12.2015.

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

Harvard

House, T, Read, JM, Danon, L & Keeling, MJ 2015, 'Testing the hypothesis of preferential attachment in social network formation', EPJ Data Science, vol. 4, no. 1, 13. https://doi.org/10.1140/epjds/s13688-015-0052-2

APA

House, T., Read, J. M., Danon, L., & Keeling, M. J. (2015). Testing the hypothesis of preferential attachment in social network formation. EPJ Data Science, 4(1), Article 13. https://doi.org/10.1140/epjds/s13688-015-0052-2

Vancouver

House T, Read JM, Danon L, Keeling MJ. Testing the hypothesis of preferential attachment in social network formation. EPJ Data Science. 2015 Dec;4(1):13. Epub 2015 Oct 9. doi: 10.1140/epjds/s13688-015-0052-2

Author

House, Thomas ; Read, Jonathan M. ; Danon, Leon et al. / Testing the hypothesis of preferential attachment in social network formation. In: EPJ Data Science. 2015 ; Vol. 4, No. 1.

Bibtex

@article{55a869f2072a458c9cc50789880f1269,
title = "Testing the hypothesis of preferential attachment in social network formation",
abstract = "The hypothesis of preferential attachment (PA) - whereby better connected individuals make more connections - is hotly debated, particularly in the context of epidemiological networks. The simplest models of PA, for example, are incompatible with the eradication of any disease through population-level control measures such as random vaccination. Typically, evidence has been sought for the presence or absence of preferential attachment via asymptotic power-law behaviour. Here, we present a general statistical method to test directly for evidence of PA in count data and apply this to data for contacts relevant to the spread of respiratory diseases. We find that while standard methods for model selection prefer a form of PA, careful analysis of the best fitting PA models allows for a level of contact heterogeneity that in fact allows control of respiratory diseases. Our approach is based on a flexible but numerically cheap likelihood-based model that could in principle be applied to other integer data where the hypothesis of PA is of interest.",
keywords = "MLE, Phase-type distribution, model selection, spectral methods, DISEASE TRANSMISSION, METABOLIC NETWORKS, POWER LAWS, IDENTIFICATION, DISTRIBUTIONS, EPIDEMIOLOGY, BEHAVIOR, MODELS",
author = "Thomas House and Read, {Jonathan M.} and Leon Danon and Keeling, {Matthew J.}",
year = "2015",
month = dec,
doi = "10.1140/epjds/s13688-015-0052-2",
language = "English",
volume = "4",
journal = "EPJ Data Science",
issn = "2193-1127",
publisher = "Springer Science + Business Media",
number = "1",

}

RIS

TY - JOUR

T1 - Testing the hypothesis of preferential attachment in social network formation

AU - House, Thomas

AU - Read, Jonathan M.

AU - Danon, Leon

AU - Keeling, Matthew J.

PY - 2015/12

Y1 - 2015/12

N2 - The hypothesis of preferential attachment (PA) - whereby better connected individuals make more connections - is hotly debated, particularly in the context of epidemiological networks. The simplest models of PA, for example, are incompatible with the eradication of any disease through population-level control measures such as random vaccination. Typically, evidence has been sought for the presence or absence of preferential attachment via asymptotic power-law behaviour. Here, we present a general statistical method to test directly for evidence of PA in count data and apply this to data for contacts relevant to the spread of respiratory diseases. We find that while standard methods for model selection prefer a form of PA, careful analysis of the best fitting PA models allows for a level of contact heterogeneity that in fact allows control of respiratory diseases. Our approach is based on a flexible but numerically cheap likelihood-based model that could in principle be applied to other integer data where the hypothesis of PA is of interest.

AB - The hypothesis of preferential attachment (PA) - whereby better connected individuals make more connections - is hotly debated, particularly in the context of epidemiological networks. The simplest models of PA, for example, are incompatible with the eradication of any disease through population-level control measures such as random vaccination. Typically, evidence has been sought for the presence or absence of preferential attachment via asymptotic power-law behaviour. Here, we present a general statistical method to test directly for evidence of PA in count data and apply this to data for contacts relevant to the spread of respiratory diseases. We find that while standard methods for model selection prefer a form of PA, careful analysis of the best fitting PA models allows for a level of contact heterogeneity that in fact allows control of respiratory diseases. Our approach is based on a flexible but numerically cheap likelihood-based model that could in principle be applied to other integer data where the hypothesis of PA is of interest.

KW - MLE

KW - Phase-type distribution

KW - model selection

KW - spectral methods

KW - DISEASE TRANSMISSION

KW - METABOLIC NETWORKS

KW - POWER LAWS

KW - IDENTIFICATION

KW - DISTRIBUTIONS

KW - EPIDEMIOLOGY

KW - BEHAVIOR

KW - MODELS

U2 - 10.1140/epjds/s13688-015-0052-2

DO - 10.1140/epjds/s13688-015-0052-2

M3 - Journal article

VL - 4

JO - EPJ Data Science

JF - EPJ Data Science

SN - 2193-1127

IS - 1

M1 - 13

ER -