A dissipative network model with neighboring activation

Xiong, Fei; Liu, Yun; Zhu, Jiasong; Zhang, Zhen Jiang; Zhang, Yan Chao; Zhang, Ying

doi:10.1140/epjb/e2011-20286-7

Cited by 15 publications

(16 citation statements)

References 28 publications

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“…It is shown that all the three types of degrees approximately exhibit power-law scaling, and the distributions in the model are qualitatively consistent with the counterparts of the empirical networks. In particular, the power law exponents for the social degrees and the favorite degree can be less than 2 in certain parameter regimes in the model, which are consistent with the empirical observations in many online social networks383940. If we do not consider the coevolution of dynamics and topology in the model, this property cannot be reproduced.…”

Section: Resultssupporting

confidence: 75%

A coevolving model based on preferential triadic closure for social media networks

Zou

Guan

et al. 2013

Sci Rep

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The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations.

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Section: Resultssupporting

confidence: 75%

A coevolving model based on preferential triadic closure for social media networks

Zou

Guan

et al. 2013

Sci Rep

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“…In other words, social users don't concentrate on the discussion all the time, and they may lose interest and drop out of it [11]. However, these dormant agents can become active again following peers [12]. Some works have verified that the final average opinion depends significantly on the external influence and internal actions.…”

Section: Introductionmentioning

confidence: 99%

Double transition in kinetic exchange opinion models with activation dynamics

Pires,

Crokidakis

2021

Preprint

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“…Then, empirical data can be further investigated using mathematical and statistical techniques. Finally, based on the properties extracted from the network, we can build mathematical models to simulate dependent processes that allow us to predict the behavior of the network (Barabási and Albert, 1999;Davidsen et al, 2002;Erdös and Rényi, 1960;Jiang et al, 2011;Kumpula et al, 2007;Leskovec, 2010;Ludwig and Abell, 2007;Marsili et al, 2004;Péter, 2012;Wang et al, 2005;White et al, 2006;Xiong et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

Modeling acquaintance networks based on balance theory

Vukašinović

Šilc

Škrekovski

2014

International Journal of Applied Mathematics and Computer Science

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An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB) model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona) network because the properties of the IB model more closely matched those of the e-mail URV network than the other models.

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A dissipative network model with neighboring activation

Cited by 15 publications

References 28 publications

A coevolving model based on preferential triadic closure for social media networks

A coevolving model based on preferential triadic closure for social media networks

Double transition in kinetic exchange opinion models with activation dynamics

Modeling acquaintance networks based on balance theory

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