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Package term October 5, 2023, Version 1.10.11 Date 20231005 Title Temporal Exponential Random Graph Models by Bootstrapped Pseudolikelihood Description Temporal Exponential Random Graph Models (TERM)
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01
Determine the variables and parameters you will be using in your temporal exponential random graph model.
02
Collect time-stamped data to use in constructing your graph model.
03
Create a matrix representation of the data, where each row represents a node and each column represents a time point.
04
Estimate the parameters of the model using maximum likelihood estimation or another suitable method.
05
Use the estimated parameters to generate a temporal exponential random graph model.

Who needs temporal exponential random graph?

01
Researchers studying dynamic social networks
02
Epidemiologists modeling the spread of diseases over time
03
Urban planners analyzing the evolution of transportation networks
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Temporal exponential random graph is a mathematical model used to analyze and simulate complex networks that evolve over time.
Researchers, social scientists, and data analysts who are studying dynamic networks are typically the ones required to file temporal exponential random graph.
Temporal exponential random graph can be filled out using specialized software or programming languages to specify the network structure, evolution rules, and parameters.
The purpose of temporal exponential random graph is to model the emergence and evolution of networks over time, and to study properties such as clustering, connectivity, and community structure.
Information such as node attributes, edge connections, timestamps, and any additional network dynamics must be reported on temporal exponential random graph.
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