Graphical networks are those that can be conceptualized as nodes connected by one or more links. Links may be directed or not. Nodes can represent many things, such as concepts, people, tasks, relationships, etc. Some are referred to as; Social Networks, or Concept Maps or Directed Graphs. In many cases of analysis, the modeling of the node linkage structure is of interest, conditional on the graph. Also, visualization and descriptive summary measures of networks graphs are also required. There are several R based http://www.rproject.org/ modeling packages availabe to address simple and complex model structures, such as, logistic random effects, latent space clusters, linear exponential random network models and many more. Two R packages in particular specialize in this area; statnet and latentnet. StatNet http://csde.washington.edu/statnet/ can handle relatively large networks of about 3,000 nodes and provides tools for both model estimation and modelbased network simulation. Latentnet is similar but provides access to latent position and cluster model structures. However if on the other hand, when your task is to uncover/discover what the graph is, conditional on observed node specific data, then consider some of the methods available in the Weka http://www.cs.waikato.ac.nz/ml/weka/ package addressing Bayes Net classification methods.

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