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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== Brief Description Of The Method == | == Brief Description Of The Method == | ||
Revision as of 00:15, 4 April 2011
Contents
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.
Brief Description Of The Method
Experimental Result
This paper complements and extends the existing literature around influence in online communities.
- Prior work has shown that how one’s friends influence the groups one joins online is quite similar across a variety of domains, content types, community goals, and ways of inferring ties. This paper shows that this type of social influence occurs in Wikipedia as well.
- The demonstration of the relationship between snapshot and ordinal-time measurements may help researchers better understand social influence by allowing them to more easily compare data gathered with different sampling procedures.
Related papers
This paper uses conclusions in Measuring wikipedia to compare with its experimental result.
Feedback effects between similarity and social influence in online communities.
