Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions emerge in real-world networks, but also for gaining insight into various rich-get-richer effects that arise in diverse fields. Conventional estimation methods require that a  network be observed across at least two snapshots in time. Numerous real-world datasets are, however, available only as single snapshots, rendering most conventional methods inapplicable of estimating a general preferential attachment function . We propose a nonparametric method, called PAFit-oneshot, for estimating preferential attachment in a growing network from one snapshot. PAFit-oneshot corrects for a previously unnoticed bias that arises when estimating preferential attachment values only for degrees observed in the  snapshot. Our work provides scientists with a means of measuring preferential attachment in a large number of publicly available one-snapshot networks. As a demonstration, we estimated preferential attachment in three such networks, and found sublinear preferential attachment in all cases  .
Preferential attachment is commonly invoked to explain the emergence of those heavy-tailed degree distributions characteristic of growing network representations of divers real-world phenomena. Experimentally confirming this hypothesis in real-world growing networks is an important frontier in network science research. Conventional preferential attachment estimation methods require that a growing network be observed across at least two snapshots in time. Numerous publicly available growing network datasets are, however, only available as single snapshots, leaving the applied network scientist with no means of measuring preferential attachment in these cases . We propose a nonparametric method, called PAFit-oneshot, for estimating preferential attachment in a growing network from one snapshot. PAFit-oneshot corrects for a previously unnoticed bias that arises when estimating preferential attachment values only for degrees observed in the single snapshot. Our work provides a means of measuring preferential attachment in a large number of publicly available one-snapshot networks. As a demonstration, we estimated preferential attachment in three such networks, and found sublinear preferential attachment in all cases . PAFit-oneshot is implemented in the R package PAFit .
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Measuring preferential attachment in growing networks has received considerable attention, not only for understanding how heavy-tail degree distributions emerge
Preferential attachment is commonly invoked to explain the emergence of those heavy-tailed degree distributions characteristic of growing network representations of divers real-world phenomena. Experimentally confirming this hypothesis
R
networks, but also for gaining insight into various rich-get-richer effects that arise in diverse fields. Conventional
growing networks is an important frontier in network science research. Conventional preferential attachment
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growing
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real-world
publicly available growing network
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available only
only available
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rendering most conventional methods inapplicable of estimating a general preferential attachment function
leaving the applied network scientist with no means of measuring preferential attachment in these cases
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single
D
scientists with
A
. PAFit-oneshot is implemented in the R package PAFit