考虑个体效用因素的社会网络演化分析模型

Utility-based model for interpreting evolution patterns of social networks

既有的社会网络演化分析模型往往通过统计分析的方法从宏观上描述网络演化规律,难以深入解释驱动网络演化的微观行为原因.为了弥补以上不足,建立了网络参与者的效用函数,并引入效用分析的方法解释网络上链路形成或断开的现象,进而揭示驱动网络演化的微观行为原因.与此同时,考虑到网络参与者见面过程是网络演化的内在过程,将其视为难以观测的潜变量引入模型,用以解释不能由效用分析所刻画的网络演化现象.在以上理论模型基础上,为将其进一步量化和应用,基于对社会网络一个时期的观察和网络参与者个体属性的数据,发展了基于贝叶斯推断的参数估计方法,校准所建立效用函数中的参数并估计潜在的见面过程.通过两组仿真分析验证了模型参数估计的准确性并讨论了模型的适用范围,并将模型应用于取自Facebook平台的真实数据集,实证了模型的解释力和预测力.本文提出的模型将有助于解释社交媒体平台上社会网络形成的原因,并预测网络演化的趋势,为进一步优化社会网络结构和控制信息传播打下模型基础.

Existing models often uncovered the evolution patterns via statistical analysis, which would be unable to explain micro behavior reasons driving the social network evolution. To make up the deficiency, a utility function of network individuals is established and a utility analysis is introduced to model the social network evolution. Meanwhile, the meeting sequence, embedded in the social network evolution, is further modeled as a latent variable in order to explain the evolution phenomenon that the mentioned utility analysis cannot explain. Subsequently, taking one-period observation of social network structure and individual attributes as the input, a Bayes-inference-based method is developed for estimating the preference parameters and the latent meeting states. Through two groups of simulation analysis, the accuracy of parameter estimation and the applicable scope are verified, and the proposed model is also applied to validate its explanatory power and predic- tive force on the collected real data from Facebook platform. In all, the proposed model will be helpful to ex- plain how social network forms on social media platforms and also to predict the tendency of social network evolution, so that it can lay a foundation for achieving the expected network structure and further controlling the information spreading within social networks.