Self-Organized Criticality in Small-World Networks Based on the Social Balance Dynamics

Meng, Qinghao

doi:10.1088/0256-307x/28/11/118901

Cited by 9 publications

(9 citation statements)

References 23 publications

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“…In this example, the spin alignment along the slowly increasing external field is balanced by spin-spin interactions and the local constraints due to the random-filed disorder. The dynamics of spin flips under the disorder induced constraints and interactions was often employed to model opinion formation [66], processes driven by social-balance [67] and other cases. For the purpose of this work, we aim to explore the impact of vacancies in the underlying network onto the multifractal spectrum of the avalanche sequences.…”

Section: The Impact Of Vacancies On Multifractal Spectrum: a Comparismentioning

confidence: 99%

Mechanisms of self-organized criticality in social processes of knowledge creation

2017

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In the online social dynamics, a robust scaling behaviour appears as a key feature of many collaborative efforts that lead to the new social value. The underlying empirical data thus offer a unique opportunity to study the origin of self-organised criticality in social systems. In contrast to physical systems in the laboratory, various human attributes of the actors play an essential role in the process along with the contents (cognitive, emotional) of the communicated artefacts. As a prototypal example, we consider the social endeavour of knowledge creation via Questions & Answers (Q&A). Using a large empirical dataset from one of such Q&A sites and theoretical modelling, we examine the temporal correlations at all scales and the role of cognitive contents to the avalanches of the knowledge creation process. Our analysis shows that the long-range correlations and the event clustering are primarily determined by the universal social dynamics, providing the external driving of the system by the arrival of new users. While the involved cognitive contents (systematically annotated in the data and observed in the model) are crucial for a fine structure of the developing knowledge networks, they only affect the values of the scaling exponents and the geometry of large avalanches and shape the multifractal spectrum. Furthermore, we find that the level of the activity of the communities that share the knowledge correlates with the fluctuations of the innovation rate, implying that the increase of innovation may serve as the active principle of self-organisation. To identify relevant parameters and unravel the role of the network evolution underlying the process in the social system under consideration, we compare the social avalanches to the avalanche sequences occurring in the field-driven physical model of disordered solids, where the factors contributing to the collective dynamics are better understood.

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Section: The Impact Of Vacancies On Multifractal Spectrum: a Comparismentioning

confidence: 99%

Mechanisms of self-organized criticality in social processes of knowledge creation

2017

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“…e emergence of the SOC on the social networks is evidenced by the avalanche-like dynamics of microposts observed in them [32][33][34][35][36][37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Dmitriev

2021

Complexity

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There is a number of studies, in which it is established that the observed flows of microposts generated by microblogging social networks (e.g., Twitter) are characterized by avalanche-like behavior. Time series of microposts depicting such streams are the time series with a power-law distribution, with 1/f noise and long memory. Despite this, there are no studies devoted to the detection and analysis of self-organized critical state, subcritical phase, and supercritical phase. The presented paper is devoted to the detection and investigation of such critical states and phases. An algorithm is proposed that allowed to detect of critical phases and critical conditions on Twitter, based on the analysis of retweets time series corresponding to the three debates of the 2016 United States Presidential Election, as the most popular debate in the history of America, collecting 84 million live views.

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“…From the moment of the SOC model emergence, this model started to be applied to describe critical phenomena in systems regardless of their nature (e.g., see the review [9] and references therein). Not an exception is the application of the theory to the description of critical phenomena in social networks (e.g., see the works [10][11][12][13]). e motivation of our investigation is the following.…”

Section: Introductionmentioning

confidence: 99%

“…e motivation of our investigation is the following. ere is a number of studies (e.g., see the works [11,[13][14][15][16][17][18][19][20]), in which it is established that the observed flows of microposts generated by microblogging social networks (e.g., Twitter) are characterized by avalanche-like behavior. Time series of microposts depicting such streams are the time series with a power-law distribution of probabilities, with 1/f noise and long memory.…”

Section: Introductionmentioning

confidence: 99%

Self‐Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

Dmitriev

Balybin

2019

Complexity

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Recently, there has been an increasing number of empirical evidence supporting the hypothesis that spread of avalanches of microposts on social networks, such as Twitter, is associated with some sociopolitical events. Typical examples of such events are political elections and protest movements. Inspired by this phenomenon, we built a phenomenological model that describes Twitter’s self-organization in a critical state. An external manifestation of this condition is the spread of avalanches of microposts on the network. The model is based on a fractional three-parameter self-organization scheme with stochastic sources. It is shown that the adiabatic mode of self-organization in a critical state is determined by the intensive coordinated action of a relatively small number of network users. To identify the critical states of the network and to verify the model, we have proposed a spectrum of three scaling indicators of the observed time series of microposts.

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Self-Organized Criticality in Small-World Networks Based on the Social Balance Dynamics

Cited by 9 publications

References 23 publications

Mechanisms of self-organized criticality in social processes of knowledge creation

Mechanisms of self-organized criticality in social processes of knowledge creation

Identification of Self‐Organized Critical State on Twitter Based on the Retweets’ Time Series Analysis

Self‐Organized Criticality on Twitter: Phenomenological Theory and Empirical Investigation Based on Data Analysis Results

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