The scaling of attention networks

Cheng-Jun Wang and Lingfei Wu

Physica A: Statistical Mechanics and its Applications, 2016, vol. 448, issue C, 196-204

Abstract: We use clicks as a proxy of collective attention and construct networks to study the temporal dynamics of attention. In particular we collect the browsing records of millions of users on 1000 Web forums in two months. In the constructed networks, nodes are threads and edges represent the switch of users between threads in an hour. The investigated network properties include the number of threads N, the number of users UV, and the number of clicks, PV. We find scaling functions PV∼UVθ1, PV∼Nθ3, and UV∼Nθ2, in which the scaling exponents are always greater than 1. This means that (1) the studied networks maintain a self-similar flow structure in time, i.e., large networks are simply the scale-up versions of small networks; and (2) large networks are more “productive”, in the sense that an average user would generate more clicks in the larger systems. We propose a revised version of Zipf’s law to quantify the time-invariant flow structure of attention networks and relate it to the observed scaling properties. We also demonstrate the applied consequences of our research: forum-classification based on scaling properties.

Keywords: Attention dynamics; Complex networks; Online behavior; Scaling; Power law (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:448:y:2016:i:c:p:196-204

DOI: 10.1016/j.physa.2015.12.081

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