Difference between revisions of "Dan Cosley, AAAI, 2010"
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In this [[Category::paper]], the authors consider two of the most fundamental definitions of influence, one based on a small set of “snapshot” observations of a social network and the other based on detailed temporal dynamics. The former is particularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter however provides a more detailed process model of how influence spreads. The authors studied the relationship between these two ways of measuring influence, in particular establishing how to infer the more detailed temporal measure from the more readily observable snapshot measure. It validates the analysis using the history of social interactions on [[Property:UsesDataset|Wikipedia]]; the result is the first large-scale study to exhibit a direct relationship between snapshot and temporal models of [[AddressesProblem::social influence]]. | In this [[Category::paper]], the authors consider two of the most fundamental definitions of influence, one based on a small set of “snapshot” observations of a social network and the other based on detailed temporal dynamics. The former is particularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter however provides a more detailed process model of how influence spreads. The authors studied the relationship between these two ways of measuring influence, in particular establishing how to infer the more detailed temporal measure from the more readily observable snapshot measure. It validates the analysis using the history of social interactions on [[Property:UsesDataset|Wikipedia]]; the result is the first large-scale study to exhibit a direct relationship between snapshot and temporal models of [[AddressesProblem::social influence]]. | ||
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| + | == Related papers == | ||
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| + | [[RelatedPaper::Feedback effects between similarity and social influence in online communities]]. | ||
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| + | [[RelatedPaper::Measuring wikipedia]] | ||
Revision as of 23:10, 3 April 2011
Citation
Dan Cosley, Daniel Huttenlocher, Jon Kleinberg, Xiangyang Lan, Siddarth Suri. Sequential Influence Models in Social Network. Association for the Advancement of Artificial Intelligence. 2010.
Online Version
http://www.cs.cornell.edu/home/kleinber/icwsm10-seq.pdf
Databases
Summary
In this paper, the authors consider two of the most fundamental definitions of influence, one based on a small set of “snapshot” observations of a social network and the other based on detailed temporal dynamics. The former is particularly useful because large-scale social network data sets are often available only in snapshots or crawls. The latter however provides a more detailed process model of how influence spreads. The authors studied the relationship between these two ways of measuring influence, in particular establishing how to infer the more detailed temporal measure from the more readily observable snapshot measure. It validates the analysis using the history of social interactions on Wikipedia; the result is the first large-scale study to exhibit a direct relationship between snapshot and temporal models of social influence.
Related papers
Feedback effects between similarity and social influence in online communities.
