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Applying modality and equivalence concepts to pattern finding in social process-produced data

Bibliographic Data

ID4694876
AuthorsRobert A Hanneman, Robert Hanneman (University of California, Riverside, corresponding author), Christian R Shelton (0000-0001-6698-7838, University of California, Riverside)
Year2011
Volume1
Issue1
Pages59-72
Publication date2011-01-01
Peer ReviewedYes
Open AccessYes
TypeARTICLE
VenueSocial Network Analysis and Mining (JOURNAL)
Journal identifiersISSN: 1869-5450 • E-ISSN: 1869-5469
PublisherSpringer Science and Business Media LLC (PUBLISHER)
DOI10.1007/s13278-010-0009-1
OpenAlexW2017361412
LanguageEN
Citations received9
References cited29

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

Data science · Information retrieval · Programming language · Relational database · Social change · Social competence · Social heuristics · Social media · Transactional analysis · World Wide Web · Complex Network Analysis Techniques · Computer Science · Data Mining Algorithms and Applications · Mathematics · Opinion Dynamics and Social Influence · Psychology · Social Psychology · Artificial Intelligence · Theoretical Computer Science

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Unique citing works9
Citations per year0,6
Citation span2011 - 2015 (5)
Citation velocityhistorical
Highly citedNo
Citation typesNeutral: 7

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