Preferential attachment with information filtering - node degree probability distribution properties

Abstract

A network growth mechanism based on a two-step preferential rule is investigated as a model of network growth in which no global knowledge of the network is required. In the first filtering step a subset of fixed size $m$ of existing nodes is randomly chosen. In the second step the preferential rule of attachment is applied to the chosen subset. The characteristics of thus formed networks are explored using two approaches: computer simulations of network growth and a theoretical description based on a master equation. The results of the two approaches are in excellent agreement. Special emphasis is put on the investigation of the node degree probability distribution. It is found that the tail of the distribution has the exponential form given by $exp(-k/m)$. Implications of the node degree distribution with such tail characteristics are briefly discussed.

v1:revtex, 7 pages, 8 figures. v2: version to appear in Physica A

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Cited by in corpus (7)

• Accelerating, hyper-accelerating, and decelerating probabilistic networks
• Preferential attachment with partial information
• Growing small-world networks based on a modified BA model
• "Winner takes it all": strongest node rule for evolution of scale free networks
• Measuring Generalized Preferential Attachment in Dynamic Social Networks
• Information slows down hierarchy growth
• Stochastic model for scale-free networks with cutoffs