Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems

Wei Pan; Wen Dong; Cebrian M.; Taemie Kim; Fowler J.H.; Pentland A.S.

首页> 外文期刊>Signal Processing Magazine, IEEE >Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems

【24h】

Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems

机译：建立人际互动中的动态影响模型：使用数据更好地推断社会系统内的影响

获取原文

获取原文并翻译 | 示例

掌桥外文数据库（机构版） >>

开具论文收录证明 >>

文献代查 >>

页面导航

摘要
著录项
相似文献
相关主题

摘要

How can we model influence between individuals in a social system, even when the network of interactions is unknown? In this article, we review the literature on the “influence model,” which utilizes independent time series to estimate how much the state of one actor affects the state of another actor in the system. We extend this model to incorporate dynamical parameters that allow us to infer how influence changes over time, and we provide three examples of how this model can be applied to simulated and real data. The results show that the model can recover known estimates of influence, it generates results that are consistent with other measures of social networks, and it allows us to uncover important shifts in the way states may be transmitted between actors at different points in time.

机译：即使互动网络未知，我们如何在社交系统中的个人之间建立影响模型？在本文中，我们回顾了有关“影响模型”的文献，该模型利用独立的时间序列来估计一个参与者的状态对系统中另一个参与者的状态有多大影响。我们扩展了该模型，以合并动态参数，从而可以推断影响力如何随时间变化，并提供了三个示例说明如何将该模型应用于模拟数据和真实数据。结果表明，该模型可以恢复已知的影响力估计值，其生成的结果与社交网络的其他度量一致，并且它使我们能够揭示状态在参与者之间在不同时间点传递的方式上的重要变化。

著录项

来源
《Signal Processing Magazine, IEEE》 |2012年第2期|p.77-86|共10页
作者
Wei Pan; Wen Dong; Cebrian M.; Taemie Kim; Fowler J.H.; Pentland A.S.;
展开▼
作者单位

展开▼
收录信息
原文格式 PDF
正文语种 eng
中图分类
关键词

相似文献

外文文献
中文文献
专利

1. Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: Group-level validation using benchmark data, neurophysiological models and human connectome project data [J] . Ryali Srikanth, Chen Tianwen, Supekar Kaustubh, Journal of Neuroscience Methods . 2016,第Null期

机译：基于多动态系统的功能磁共振成像中因果相互作用的估计：使用基准数据，神经生理学模型和人类连接组项目数据进行组级验证
2. Multiple scales modelling approaches to social interaction in crowd dynamics and crisis management. Comment on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management" by Nicola Bellomo et al. [J] . Trucu Dumitru Physics of life reviews . 2016,第Null期

机译：人群动态和危机管理中社交互动的多尺度建模方法。 Nicola Bellomo等人对“疏散人群动态中的人类行为：从建模到“大数据”到危机管理”的评论。
3. On the complex interaction between social sciences and crowd dynamics Comment on "Human behaviours in evacuation crowd dynamics: From modelling to "big data" toward crisis management" by Nicola Bellomo et al. [J] . Dolfin Marina Physics of life reviews . 2016,第Null期

机译：关于社会科学与人群动力学之间的复杂相互作用，Nicola Bellomo等人评论了“疏散人群动力学中的人类行为：从建模到“大数据”到危机管理”。
4. Social Influence Analysis Based on Modeling Interactions in Dynamic Social Networks: A Case Study [C] . Liwei Huang, Yutao Ma, Yanbo Liu International Conference on Cloud Computing and Security . 2016

机译：动态社交网络中基于交互建模的社会影响力分析：一个案例研究
5. Statistical inference for dynamical, interacting multi-object systems with emphasis on human small group interactions. [D] . Rozgic, Viktor. 2011

机译：动态交互的多对象系统的统计推断，重点是人类小组互动。
6. Multivariate dynamical systems-based estimation of causal brain interactions in fMRI: Group-level validation using benchmark data neurophysiological models and human connectome project data [O] . Srikanth Ryali, Tianwen Chen, Kaustubh Supekar, -1

机译：基于多动态系统的功能磁共振成像中因果相互作用的估计：使用基准数据神经生理学模型和人类连接组项目数据进行组级验证
7. Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems [O] . Pan, Wei, Dong, Wen, Cebrian, Manuel, 2012

机译：模拟人类交互中的动态影响：使用数据更好地推断社会系统中的影响

Modeling Dynamical Influence in Human Interaction: Using data to make better inferences about influence within social systems

摘要

著录项

相似文献

相关主题

期刊订阅