Social Influence in Signed Networks

He, Xiaochen; Lu, Jiali; Hai-feng, DU; Jin, Xiaoyi

doi:10.1109/tcss.2022.3220944

Cited by 4 publications

(4 citation statements)

References 86 publications

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“…Social influence has long been extensively studied because of its potential to aggregate collective behaviors, which may have dramatic consequences in economics, politics and cultures. Specifically, social influence in market system can form a path-dependent process in which the early popularity advantage of an object can be perpetuated by driving feedback, thus affecting later individuals’ choices [ 26 ].…”

Section: Methodsmentioning

confidence: 99%

“…The classic Pólya urn model is a simple stochastic process based on a ball-drawing process, at each time, a ball is randomly drawn from the urn and then return along with a ball of the same color. Since balls with the majority color are more likely to be drawn, resulting in the majority color gradually dominating the entire urn, which forms a self-reinforcement path for the dynamics of social influence [ 18 , 24 – 26 ]. Urn model has been extensively applied to delineate diverse phenomena [ 27 ], including the species evolution process [ 28 ], game strategy and decision-making [ 29 ], the dynamics of novelties and innovations [ 30 ], tourism activities and voting forecasting [ 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

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A hypernetwork-based urn model for explaining collective dynamics

Lu,

Du,

2023

PLoS ONE

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The topological characterization of complex systems has significantly contributed to our understanding of the principles of collective dynamics. However, the representation of general complex networks is not enough for explaining certain problems, such as collective actions. Considering the effectiveness of hypernetworks on modeling real-world complex networks, in this paper, we proposed a hypernetwork-based Pólya urn model that considers the effect of group identity. The mathematical deduction and simulation experiments show that social influence provides a strong imitation environment for individuals, which can prevent the dynamics from being self-correcting. Additionally, the unpredictability of the social system increases with growing social influence, and the effect of group identity can moderate market inequality caused by individual preference and social influence. The present work provides a modeling basis for a better understanding of the logic of collective dynamics.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A hypernetwork-based urn model for explaining collective dynamics

Lu,

Du,

2023

PLoS ONE

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“…In particular, in the context of randomly growing graphs, the reinforcements favor a high-degree vertex in getting an even higher degree as the network grows. Indeed, Pólya urn models have been widely used to model reinforcements, for instance, in communication channels (Alajaji and Fuja, 1994), image segmentation (Banerjee et al, 1999), social and epidemic networks (Avin et al, 2020;Kim and Jo, 2010;Toivonen et al, 2006;Hayhoe et al, 2018;Singh et al, 2022c, a, b;Jadbabaie et al, 2023;He et al, 2023), citation networks (Jeong et al, 2003;Milojević, 2010;Newman, 2001), actor collaborations (Albert and Barabási, 2000;Jeong et al, 2003), mechanics (Albert and Barabási, 2002), and polymer formations (Bhutani et al, 2022). Naturally, Pólya urns have been used to model preferential attachment graphs in the literature.…”

Section: Introductionmentioning

confidence: 99%

Generating preferential attachment graphs via a Pólya urn with expanding colors

Singh,

Alajaji,

Gharesifard

2024

Net Sci

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We introduce a novel preferential attachment model using the draw variables of a modified Pólya urn with an expanding number of colors, notably capable of modeling influential opinions (in terms of vertices of high degree) as the graph evolves. Similar to the Barabási-Albert model, the generated graph grows in size by one vertex at each time instance; in contrast however, each vertex of the graph is uniquely characterized by a color, which is represented by a ball color in the Pólya urn. More specifically at each time step, we draw a ball from the urn and return it to the urn along with a number of reinforcing balls of the same color; we also add another ball of a new color to the urn. We then construct an edge between the new vertex (corresponding to the new color) and the existing vertex whose color ball is drawn. Using color-coded vertices in conjunction with the time-varying reinforcing parameter allows for vertices added (born) later in the process to potentially attain a high degree in a way that is not captured in the Barabási-Albert model. We study the degree count of the vertices by analyzing the draw vectors of the underlying stochastic process. In particular, we establish the probability distribution of the random variable counting the number of draws of a given color which determines the degree of the vertex corresponding to that color in the graph. We further provide simulation results presenting a comparison between our model and the Barabási-Albert network.

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“…Gao et al [1] proposed the dimension reduction method that decomposes N-dimensional networks into one-dimensional effective models and uses it to predict the global activity of the original network. Subsequently, based on the theoretical tools for large-scale networks and the advanced data analyzing techniques [10][11][12][13], the method has been extended to many aspects such as noise effects [14][15][16], reduction methods based on spectral dimension [17], sequence mean field [18] and degree weighted average [19], and has been applied to various fields [20][21][22]. The dynamic characteristics of a system strongly depend on the underlying network structure [17].…”

Section: Introductionmentioning

confidence: 99%

Effect of network structure on the accuracy of resilience dimension reduction

Liu,

Guo,

Liu

2024

Front. Phys.

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Dimension reduction is an effective method for system’s resilience analysis. In this paper, we investigate the effect of network structure on the accuracy of resilience dimension reduction. First, we introduce the resilience dimension reduction method and define the evaluation indicator of the resilience dimension reduction method. Then, by adjusting node connections, preferential connection mechanisms, and connection probabilities, we generate artificial networks, small-world networks and social networks with tunable assortativity coefficients, average clustering coefficients, and modularities, respectively. Experimental results for the gene regulatory dynamics show that the network structures with positive assortativity, large clustering coefficient, and significant community can enhance the accuracy of resilience dimension reduction. The result of this paper indicates that optimizing network structure can enhance the accuracy of resilience dimension reduction, which is of great significance for system resilience analysis and provides a new perspective and theoretical basis for selecting dimension reduction methods in system resilience analysis.

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Social Influence in Signed Networks

Cited by 4 publications

References 86 publications

A hypernetwork-based urn model for explaining collective dynamics

A hypernetwork-based urn model for explaining collective dynamics

Generating preferential attachment graphs via a Pólya urn with expanding colors

Effect of network structure on the accuracy of resilience dimension reduction

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