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- 980 creator jennifer-neville.
- 980 creator timothy-la-fond.
- 980 type InProceedings.
- 980 label "Randomization tests for distinguishing social influence and homophily effects".
- 980 sameAs 980.
- 980 abstract "Relational autocorrelation is ubiquitous in relational domains. This observed correlation between class labels of linked instances in a network (e.g., two friends are more likely to share political beliefs than two randomly selected people) can be due to the effects of two different social processes. If \emph{social influence} effects are present, instances are likely to change their attributes to conform to their neighbor values. If \emph{homophily} effects are present, instances are likely to link to other individuals with similar attribute values. Both these effects will result in autocorrelated attribute values. When analyzing static relational networks it is impossible to determine how much of the observed correlation is due each of these factors. However with the recent surge of interest in social networks, the availability of dynamic network data has increased. In this paper, we present a randomization technique for temporal network data where the attributes and links change over time. Given data from two time steps, we measure the gain in correlation and assess whether a significant portion of this gain is due to influence and/or homophily. We demonstrate the efficacy of our method on semi-synthetic data and then apply the method to a real-world social networks dataset, showing the impact of both influence and homophily effects.".
- 980 hasAuthorList authorList.
- 980 isPartOf proceedings.
- 980 keyword "Social data analysis".
- 980 keyword "analytics".
- 980 title "Randomization tests for distinguishing social influence and homophily effects".