Scale-free network growth by ranking.

By Santo Fortunato, Alessandro Flammini, Filippo Menczer. 2006

135 citations recorded by the source corpus.

Network growth is currently explained through mechanisms that rely on node prestige measures, such as degree or fitness. In many real networks, those who create and connect nodes do not know the prestige values of existing nodes but only their ranking by prestige. We propose a criterion of network growth that explicitly relies on the ranking of the nodes according to any prestige measure, be it topological or not. The resulting network has a scale-free degree distribution when the probability to link a target node is any power-law function of its rank, even when one has only partial information of node ranks. Our criterion may explain the frequency and robustness of scale-free degree distributions in real networks, as illustrated by the special case of the Web graph.

Subfields: Management Science and Operations Research, Statistical and Nonlinear Physics

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Citations in the corpus

How algorithmic popularity bias hinders or promotes quality (2017, positive): “of selection probability for lower-ranked items. This schema is inspired by the ranking model, which allows for the emergence of scale-free popularity distributions with arbitrary power-law exponents [11]; it is consistent with empirical data about how people click search engine results [10] and scr”
Popularity versus similarity in growing networks (2011, background): “Another interesting observation was made in [14] where the authors show that connections based solely on node ranking may lead to power laws, the motivation being that node ranking is a coarser proxy to popularity than the node degree.”