document: Feedback effects between similarity and social influence in online communities A fundamental open question in the analysis of social networks is to understand the interplay between similarity and social ties . People are similar to their neighbors in a social network for two distinct reasons : first , they grow to resemble their current friends due to social influence ; and second , they tend to form new links to others who are already like them , a process often termed selection by sociologists . While both factors are present in everyday social processes , they are in tension : social influence can push systems toward uniformity of behavior , while selection can lead to fragmentation . As such , it is important to understand the relative effects of these forces , and this has been a challenge due to the difficulty of isolating and quantifying them in real settings . We develop techniques for identifying and modeling the interactions between social influence and selection , using data from online communities where both social interaction and changes in behavior over time can be measured . We find clear feedback effects between the two factors , with rising similarity between two individuals serving , in aggregate , as an indicator of future interaction -- but with similarity then continuing to increase steadily , although at a slower rate , for long periods after initial interactions . We also consider the relative value of similarity and social influence in modeling future behavior . For instance , to predict the activities that an individual is likely to do next , is it more useful to know the current activities of their friends , or of the people most similar to them ?
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