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信任对信息传播的影响。

Influence of trust in the spreading of information.

机构信息

State Key Laboratory of Software Engineering, Wuhan University, 430072 Wuhan, China.

Departament d'Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili, 43007 Tarragona, Spain.

出版信息

Phys Rev E. 2017 Jan;95(1-1):012301. doi: 10.1103/PhysRevE.95.012301. Epub 2017 Jan 3.

DOI:10.1103/PhysRevE.95.012301
PMID:28208447
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7217509/
Abstract

The understanding and prediction of information diffusion processes on networks is a major challenge in network theory with many implications in social sciences. Many theoretical advances occurred due to stochastic spreading models. Nevertheless, these stochastic models overlooked the influence of rational decisions on the outcome of the process. For instance, different levels of trust in acquaintances do play a role in information spreading, and actors may change their spreading decisions during the information diffusion process accordingly. Here, we study an information-spreading model in which the decision to transmit or not is based on trust. We explore the interplay between the propagation of information and the trust dynamics happening on a two-layer multiplex network. Actors' trustable or untrustable states are defined as accumulated cooperation or defection behaviors, respectively, in a Prisoner's Dilemma setup, and they are controlled by a memory span. The propagation of information is abstracted as a threshold model on the information-spreading layer, where the threshold depends on the trustability of agents. The analysis of the model is performed using a tree approximation and validated on homogeneous and heterogeneous networks. The results show that the memory of previous actions has a significant effect on the spreading of information. For example, the less memory that is considered, the higher is the diffusion. Information is highly promoted by the emergence of trustable acquaintances. These results provide insight into the effect of plausible biases on spreading dynamics in a multilevel networked system.

摘要

理解和预测网络信息传播过程是网络理论中的一个主要挑战,在社会科学中有许多影响。由于随机传播模型的出现,许多理论取得了进展。然而,这些随机模型忽略了理性决策对过程结果的影响。例如,对熟人的信任程度确实会在信息传播中发挥作用,并且参与者可能会相应地改变他们在信息传播过程中的传播决策。在这里,我们研究了一种基于信任的信息传播模型。我们探讨了信息传播和在双层多重网络上发生的信任动态之间的相互作用。参与者可信赖或不可信赖的状态被定义为在囚徒困境设置中积累的合作或背叛行为,它们由记忆跨度控制。信息传播被抽象为信息传播层上的一个阈值模型,其中阈值取决于参与者的可信赖性。使用树逼近对模型进行了分析,并在同质和异质网络上进行了验证。结果表明,先前行为的记忆对信息传播有显著影响。例如,考虑的记忆越少,扩散就越高。可信熟人的出现极大地促进了信息的传播。这些结果提供了对多层次网络系统中可能存在的偏差对传播动态的影响的深入了解。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/aa5f85e86d18/e012301_5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/568afc2b18c6/e012301_1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/d0f6971e25ac/e012301_2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/81d6000a2690/e012301_3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/2862c085c4ea/e012301_4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/aa5f85e86d18/e012301_5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/568afc2b18c6/e012301_1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/d0f6971e25ac/e012301_2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/81d6000a2690/e012301_3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/2862c085c4ea/e012301_4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/81b4/7217509/aa5f85e86d18/e012301_5.jpg

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