An evolving network model with information filtering and mixed attachment mechanisms

Xikun Huang and Ruqian Lu

Physica A: Statistical Mechanics and its Applications, 2020, vol. 545, issue C

Abstract: In this paper, we propose an evolving network model with information filtering and mixed attachment mechanisms. We theoretically analyze the in-degree distribution of networks generated by the proposed model in two special cases, and prove that the in-degree distribution is power-law if the new coming vertex does not filter information. Otherwise, the in-degree distribution becomes complex which has a transition between exponential and power-law scaling. Numerical simulations are consistent with analytical results. In addition, we analyze how model parameters influence the topology of the network by means of numerical simulations, and compare values of various measures of networks generated by the proposed model with that of networks generated by uniform attachment model, Barabási–Albert model and copying model. It shows that our model can generate networks with more diverse topological features.

Keywords: Network generative model; Information filtering; Preferential attachment; Copying; In-degree distribution (search for similar items in EconPapers)
Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:545:y:2020:i:c:s0378437119319107

DOI: 10.1016/j.physa.2019.123421

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