UCR

Computer Science and Engineering



Christian R. Shelton, Professor

Applying Modality and Equivalence Concepts to Pattern-Finding in Social Process-Produced Data (2011)

by Robert A. Hanneman and Christian R. Shelton


Abstract: Large amounts of detailed transactional information are generated by ongoing social processes. For managing and mining such data, we treat them as ``objects'' and ``relations''. These ideas strongly parallel the way that social network analysts conceive of social structure. Modality (roughly, distinguishing multiple classes of social actors or nodes in networks) and equivalence classes (roughly, distinguishing general patterns in the ways that objects in classes are related to one another or to objects in other classes) have proven to be very useful in helping social network analysts to think about complex relational structures among social objects. Dimensional and generalized ``block models'' of multi-modal social networks provide tools for designing searches to identify patterns. The ideas are illustrated by descriptions of how a number of social process-produced data might be approached, including bibliographic databases, communications logs, virtual communities, and economic transactions.

Download Information

Robert A. Hanneman and Christian R. Shelton (2011). "Applying Modality and Equivalence Concepts to Pattern-Finding in Social Process-Produced Data." Social Network Analysis and Mining, 1, 59-72. pdf          

Bibtex citation

@article{HanShe11,
   author = "Robert A. Hanneman and Christian R. Shelton",
   title = "Applying Modality and Equivalence Concepts to Pattern-Finding in Social Process-Produced Data",
   journal = "Social Network Analysis and Mining",
   year = 2011,
   volume = 1,
   pages = "59--72",
}

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E-mail: cshelton@cs.ucr.edu

 


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