Asymmetric access to information impacts the power-law exponent in networks

Abstract

The preferential attachment (PA) process is a popular theory for explaining network power-law degree distributions. In PA, the probability that a new vertex adds an edge to an existing vertex depends on the connectivity of the target vertex. In real-world networks, however, each vertex may have asymmetric accessibility to information. Here we address this issue using a new network-generation mechanism that incorporates asymmetric accessibility to upstream and downstream information. We show that this asymmetric information accessibility directly affects the power-law exponent, producing a broad range of values that are consistent with observations. Our findings shed new light on the possible mechanisms in three important real-world networks: a citation network, a hyperlink network, and an online social network.

4 pages, 3 figures

References in corpus (15)

• Emergence of scaling in random networks
• Statistical mechanics of complex networks
• Power-law distributions in empirical data
• Power laws, Pareto distributions and Zipf's law
• Clustering and preferential attachment in growing networks
• The Web of Human Sexual Contacts
• Measuring preferential attachment for evolving networks
• Preferential attachment in the growth of social networks: the case of Wikipedia
• Preferential attachment in the protein network evolution
• Log-Networks
• Modes of Collaboration in Modern Science - Beyond Power Laws and Preferential Attachment
• Edge vulnerability in neural and metabolic networks
• Inheritance patterns in citation networks reveal scientific memes
• Life, Death and Preferential Attachment
• A Generalized Preferential Attachment Model for Business Firms Growth Rates: I. Empirical Evidence

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