document: 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 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 .
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