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Continuous-Time Regression Models for Longitudinal Networks
Published on Jan 25, 20123849 Views
The development of statistical models for continuous-time longitudinal network data is of increasing interest in machine learning and social science. Leveraging ideas from survival and event history a
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Chapter list
Continuous-Time Regression Models for Longitudinal Networks00:00
Motivation00:08
Outline - 102:07
Outline - 202:34
Counting Processes for Networks02:35
Egocentric Example: Citation Networks03:35
Relational Example: Social Networks04:34
Multivariate Counting Process05:33
Doob-Meyer Decomposition06:11
Outline - 306:49
Modeling the Intensity Process06:54
Network Statistics09:01
Outline - 409:37
Partial Likelihood (fitting the Cox model) - 109:41
Partial Likelihood (fitting the Cox model) - 210:05
Least Squares (fitting the Aalen model)10:36
Network Data Sets11:47
Recovering time-varying coefficients12:03
Irvine data set12:51
MetaFilter data set13:44
Summary14:22
References14:57