摘要
随着社交网络平台的快速发展,带有网络结构的时序数据越来越多.为拟合用户行为的动态变化,网络向量自回归模型被提出.模型最早研究的是连续型因变量.然而实际数据常观测到离散型因变量.由此,本文提出广义网络向量自回归模型.模型假设存在一个潜在的连续型变量,决定了可观测到的离散型因变量的取值.为了估计和推断模型,本文提出了MCMC (Markov chain Monte Carlo)算法并通过随机模拟进行验证.最后,使用某社交网络平台上的两个真实的数据案例作为例证.
With the rapid development of social network platforms, the time series of network data is becoming increasingly available. To model the dynamic user behaviors, a network vector autoregression model is developed,which targets at the continuous type responses. In practice, the discrete type of data(e.g., numbers of posts, user decisions) are frequently collected from the network users. To model such a type of data, we propose a generalized network vector autoregression model in this work. It assumes that a latent continuous variable exists for each node at each time point, which determines the observed response variable. The dynamic and network dependence is assumed based on the latent variables(states). To estimate and make a valid inference of the model, an MCMC(Markov chain Monte Carlo) algorithm is designed and verified by extensive numerical studies. Two real data examples are presented using datasets from a social network platform for illustration.
作者
王菲菲
朱雪宁
潘蕊
Feifei Wang;Xuening Zhu;Rui Pan
出处
《中国科学：数学》
CSCD
北大核心
2021年第8期1253-1266,共14页
Scientia Sinica：Mathematica
基金
国家自然科学基金(批准号:11901105,11690015,U1811461和11971504)
中国人民大学科学研究基金(中央高校基本科研业务费专项资金)(批准号:18XNLG02)
上海市科技人才计划(批准号:19YF1402700)
复旦新再灵大数据联合实验室
中央财经大学“青年英才”项目(批准号:QYP1911)
中央高校基本科研业务费(批准号:20190107)资助项目。
