Co-evolution of networks and quantum dynamics: a generalization of the Barabási-Albert model of preferential attachment

Edwin Hancock; Norio Konno; Vito Latora; Takuya Machida; Vincenzo Nicosia; Simone Severini; Richard Wilson

Co-evolution of networks and quantum dynamics: a generalization of the Barabási-Albert model of preferential attachment

Edwin Hancock, Norio Konno, Vito Latora, Takuya Machida, Vincenzo Nicosia, Simone Severini, Richard Wilson

Computer Science

Research output: Working paper › Preprint

Abstract

We propose a network growth algorithm based on the dynamics of a quantum mechanical system co-evolving together with a graph which is seen as its phase space. The algorithm naturally generalizes Barab\'asi-Albert model of preferential attachment and it has a rich set of tunable parameters -- for example, the initial conditions of the dynamics or the interaction of the system with its environment. We observe that the algorithm can grow networks with two-modal power-law degree distributions and super-hubs.

Original language	English
Publisher	Arxiv (Cornell University)
Number of pages	10
Publication status	Published - 4 Feb 2013

Access to Document

pdfSubmitted manuscript, 243 KB

http://arxiv.org/abs/1302.0887v1

Cite this

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abstract = "We propose a network growth algorithm based on the dynamics of a quantum mechanical system co-evolving together with a graph which is seen as its phase space. The algorithm naturally generalizes Barab\'asi-Albert model of preferential attachment and it has a rich set of tunable parameters -- for example, the initial conditions of the dynamics or the interaction of the system with its environment. We observe that the algorithm can grow networks with two-modal power-law degree distributions and super-hubs.",

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AU - Konno, Norio

AU - Latora, Vito

AU - Machida, Takuya

AU - Nicosia, Vincenzo

AU - Severini, Simone

AU - Wilson, Richard

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AB - We propose a network growth algorithm based on the dynamics of a quantum mechanical system co-evolving together with a graph which is seen as its phase space. The algorithm naturally generalizes Barab\'asi-Albert model of preferential attachment and it has a rich set of tunable parameters -- for example, the initial conditions of the dynamics or the interaction of the system with its environment. We observe that the algorithm can grow networks with two-modal power-law degree distributions and super-hubs.

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PB - Arxiv (Cornell University)

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