Onymity promotes cooperation in social dilemma experiments

Wang, Zhen; Jusup, Marko; Wang, Rui Wu; Ши, Лей; Iwasa, Yoh; Moreno, Yamir; Kurths, Jürgen

doi:10.1126/sciadv.1601444

Cited by 228 publications

(67 citation statements)

References 38 publications

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“…Our findings enrich our understanding of how social contagions transmit through communal systems. Our theory in this work can be used to study epidemic spreading [6,[49][50][51], the effects of vaccination [52], and the impact of human behavior [53,54] on epidemics. In future work we will further explore our approach using real social contagion data and digital virtual asset security.…”

Section: Discussionmentioning

confidence: 99%

Optimal community structure for social contagions

Wang

et al. 2018

New J. Phys.

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Community structure is an important factor in the behavior of real-world networks because it strongly affects the stability and thus the phase transition order of the spreading dynamics. We here propose a reversible social contagion model of community networks that includes the factor of social reinforcement. In our model an individual adopts a social contagion when the number of received units of information exceeds its adoption threshold. We use meanfield approximation to describe our proposed model, and the results agree with numerical simulations. The numerical simulations and theoretical analyses both indicate that there is a first-order phase transition in the spreading dynamics, and that a hysteresis loop emerges in the system when there is a variety of initially-adopted seeds. We find an optimal community structure that maximizes spreading dynamics. We also find a rich phase diagram with a triple point that separates the no-diffusion phase from the two diffusion phases.

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Section: Discussionmentioning

confidence: 99%

Optimal community structure for social contagions

Wang

et al. 2018

New J. Phys.

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“…Definition 3.4. The individuation of one method is defined as follows, 14) where N S (·) is the number of nodes with unique score, |N | is the number of nodes in the entire network. γ(·) is the individuation of one method.…”

Section: Experiments Ii: Individuationmentioning

confidence: 99%

Vital spreaders identification in complex networks with multi-local dimension

Wen

Pelusi

Deng

2020

Knowledge-Based Systems

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The important nodes identification has been an interesting problem in this issue. Several centrality measures have been proposed to solve this problem, but most of previous methods have their own limitations. To address this problem more effectively, multi-local dimension (MLD) which is based on the fractal property is proposed to identify the vital spreaders in this paper. This proposed method considers the information contained in the box and q plays a weighting coefficient for this partition information. MLD would have different expressions with different value of q, and it would degenerate to local information dimension and variant of local dimension when q = 1 when q = 0 respectively, both of which have been effective identification method for influential nodes. Thus, MLD would be a more general method which can degenerate to some exiting centrality measures. In addition, different with classical methods, the node with low MLD would be more important in the network. Some comparison methods and real-world complex networks are applied in this paper to show the effectiveness and reasonableness of this proposed method. The experiment results show the superiority of this proposed method.

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“…Because the complex network [6] can show the structural complexity and the original properties of the original system, it is developing rapidly and wildly used in many natural sciences over the years. The study of complex network can help to quantify the fuzzy world, predict the development of the system [7,8], and study the prisoner's dilemma experiments [9][10][11]. Complex networks have produced a large number of practical models and these have achieved a lot of practical results [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Identifying influential nodes based on fuzzy local dimension in complex networks

Wen

Jiang

2019

Chaos, Solitons & Fractals

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How to identify influential nodes in complex networks is an important aspect in the study of complex network. In this paper, a novel fuzzy local dimension (FLD) is proposed to rank the influential nodes in complex networks, where a node with high fuzzy local dimension has high influential ability. This proposed method focuses on the influence of the distance from the center node on the local dimension of center node by fuzzy set, resulting in a change in influential ability. In order to show this proposed method's effectiveness and accuracy, four real-world networks are applied in this paper. Meanwhile, Susceptible-Infected (SI) is used to simulate the spreading process by FLD and other centrality measures, and Kendall's tau coefficient is used to describe the correlation between the influential nodes obtained by centrality and the results measured by SI model. Observing from the ranking lists and simulated results, this method is effective and accurate to rank the influential nodes.

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Onymity promotes cooperation in social dilemma experiments

Abstract: Researchers test whether and how onymity, as opposed to anonymity, promotes cooperation in a social dilemma experiment.

Cited by 228 publications

References 38 publications

Optimal community structure for social contagions

Optimal community structure for social contagions

Vital spreaders identification in complex networks with multi-local dimension

Identifying influential nodes based on fuzzy local dimension in complex networks

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