Modeling online social signed networks

Le Li, Ke Gu, An Zeng, Ying Fan and Zengru Di

Physica A: Statistical Mechanics and its Applications, 2018, vol. 495, issue C, 345-352

Abstract: People’s online rating behavior can be modeled by user–object bipartite networks directly. However, few works have been devoted to reveal the hidden relations between users, especially from the perspective of signed networks. We analyze the signed monopartite networks projected by the signed user–object bipartite networks, finding that the networks are highly clustered with obvious community structure. Interestingly, the positive clustering coefficient is remarkably higher than the negative clustering coefficient. Then, a Signed Growing Network model (SGN) based on local preferential attachment is proposed to generate a user’s signed network that has community structure and high positive clustering coefficient. Other structural properties of the modeled networks are also found to be similar to the empirical networks.

Keywords: Signed networks; Online rating systems; Growing network model (search for similar items in EconPapers)
Date: 2018
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:495:y:2018:i:c:p:345-352

DOI: 10.1016/j.physa.2017.12.089

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