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