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2010
ACM

Randomization tests for distinguishing social influence and homophily effects

8 years 6 months ago
Randomization tests for distinguishing social influence and homophily effects
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 social influence effects are present, instances are likely to change their attributes to conform to their neighbor values. If 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, the recent surge of interest in social networks has increased the availability of dynamic network data. In this paper, we present a randomization technique for temporal network data where the attributes and links change over time. Given data...
Timothy La Fond, Jennifer Neville
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where WWW
Authors Timothy La Fond, Jennifer Neville
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