On Skewed Distributions and Straight Lines

Mokryn, Osnat; Reznik, Alexey

doi:10.1145/2740908.2744716

Cited by 2 publications

(1 citation statement)

References 20 publications

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“…While initial citation distribution is close to log-normal, the evolving distributions straighten and become closer to power-law. This corroborates the conjecture of Mokryn and Reznik [45] who assumed that the power-law degree distribution can be found only in static networks, namely, those that underwent a long period of development. We show here that citation distributions, once considered as static, are in fact transient.…”

Section: Discussionsupporting

confidence: 90%

Power-Law Citation Distributions are Not Scale-Free

Golosovsky¹

2019

SpringerBriefs in Complexity

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We analyze time evolution of statistical distributions of citations to scientific papers published in one year. While these distributions can be fitted by a power-law dependence we find that they are nonstationary and the exponent of the power law fit decreases with time and does not come to saturation. We attribute the nonstationarity of citation distributions to different longevity of the low-cited and highly-cited papers. By measuring citation trajectories of papers we found that citation careers of the low-cited papers come to saturation after 10-15 years while those of the highly-cited papers continue to increase indefinitely: the papers that exceed some citation threshold become runaways. Thus, we show that although citation distribution can look as a power-law, it is not scale-free and there is a hidden dynamic scale associated with the onset of runaways. We compare our measurements to our recently developed model of citation dynamics based on copying/redirection/triadic closure and find explanations to our empirical observations.

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Section: Discussionsupporting

confidence: 90%

Power-Law Citation Distributions are Not Scale-Free

Golosovsky¹

2019

SpringerBriefs in Complexity

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The Role of Temporal Trends in Growing Networks

Mokryn¹,

Wagner²,

Blattner³

et al. 2016

PLoS ONE

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The rich get richer principle, manifested by the Preferential attachment (PA) mechanism, is widely considered one of the major factors in the growth of real-world networks. PA stipulates that popular nodes are bound to be more attractive than less popular nodes; for example, highly cited papers are more likely to garner further citations. However, it overlooks the transient nature of popularity, which is often governed by trends. Here, we show that in a wide range of real-world networks the recent popularity of a node, i.e., the extent by which it accumulated links recently, significantly influences its attractiveness and ability to accumulate further links. We proceed to model this observation with a natural extension to PA, named Trending Preferential Attachment (TPA), in which edges become less influential as they age. TPA quantitatively parametrizes a fundamental network property, namely the network’s tendency to trends. Through TPA, we find that real-world networks tend to be moderately to highly trendy. Networks are characterized by different susceptibilities to trends, which determine their structure to a large extent. Trendy networks display complex structural traits, such as modular community structure and degree-assortativity, occurring regularly in real-world networks. In summary, this work addresses an inherent trait of complex networks, which greatly affects their growth and structure, and develops a unified model to address its interaction with preferential attachment.

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On Skewed Distributions and Straight Lines

Cited by 2 publications

References 20 publications

Power-Law Citation Distributions are Not Scale-Free

Power-Law Citation Distributions are Not Scale-Free

The Role of Temporal Trends in Growing Networks

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