用于不断演变的社会互动的自适应时间因果网络模型的动力学建模与分析。
Modelling and analysis of the dynamics of adaptive temporal-causal network models for evolving social interactions.
作者信息
Treur Jan
机构信息
Behavioural Informatics Group, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
出版信息
Comput Soc Netw. 2017;4(1):4. doi: 10.1186/s40649-017-0039-1. Epub 2017 Jun 12.
BACKGROUND
Network-Oriented Modelling based on adaptive temporal-causal networks provides a unified approach to model and analyse dynamics and adaptivity of various processes, including mental and social interaction processes.
METHODS
Adaptive temporal-causal network models are based on causal relations by which the states in the network change over time, and these causal relations are adaptive in the sense that they themselves also change over time.
RESULTS
It is discussed how modelling and analysis of the dynamics of the behaviour of these adaptive network models can be performed. The approach is illustrated for adaptive network models describing social interaction.
CONCLUSIONS
In particular, the homophily principle and the 'more becomes more' principles for social interactions are addressed. It is shown how the chosen Network-Oriented Modelling method provides a basis to model and analyse these social phenomena.
背景
基于自适应时间因果网络的面向网络建模提供了一种统一的方法，用于对包括心理和社会互动过程在内的各种过程的动态性和适应性进行建模和分析。
方法
自适应时间因果网络模型基于因果关系，网络中的状态随时间变化，并且这些因果关系具有适应性，即它们自身也随时间变化。
结果
讨论了如何对这些自适应网络模型的行为动态进行建模和分析。以描述社会互动的自适应网络模型为例进行了说明。
结论
特别讨论了社会互动中的同质性原则和“越多越更多”原则。展示了所选择的面向网络建模方法如何为建模和分析这些社会现象提供基础。
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