MAXIMUM LIKELIHOOD ESTIMATION FOR SOCIAL NETWORK DYNAMICS.
MAXIMUM LIKELIHOOD ESTIMATION FOR SOCIAL NETWORK DYNAMICS.
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DOI：
10.1214/09-aoas313
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发表时间：
2010-06-01
期刊：
影响因子：
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通讯作者：
Schweinberger M
中科院分区：
文献类型：
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作者：
Snijders TA;Koskinen J;Schweinberger M
本文讨论了一个网络面板数据模型，该模型假设观测数据是给定节点集上所有有向图空间上连续时间马尔可夫过程的离散观测，其中关系变量的变化与当前图无关。领带变化的模型是参数化的，设计用于社交网络分析的应用，其中网络动态可以被解释为由图的节点所表示的社会参与者所做的选择所生成。基于数据增广和随机逼近，提出了一种计算极大似然估计的算法。给出了对不断发展的友谊网络的应用，并进行了小型模拟研究，表明对于小数据集，最大似然估计比早期提出的矩量法估计更有效。
A model for network panel data is discussed, based on the assumption that the observed data are discrete observations of a continuous-time Markov process on the space of all directed graphs on a given node set, in which changes in tie variables are independent conditional on the current graph. The model for tie changes is parametric and designed for applications to social network analysis, where the network dynamics can be interpreted as being generated by choices made by the social actors represented by the nodes of the graph. An algorithm for calculating the Maximum Likelihood estimator is presented, based on data augmentation and stochastic approximation. An application to an evolving friendship network is given and a small simulation study is presented which suggests that for small data sets the Maximum Likelihood estimator is more efficient than the earlier proposed Method of Moments estimator.