document: Using proximity to predict activity in social networks The structure of a social network contains information useful for predicting its evolution . We show that structural information also helps predict activity . People who are `` close '' in some sense in a social network are more likely to perform similar actions than more distant people . We use network proximity to capture the degree to which people are `` close '' to each other . In addition to standard proximity metrics used in the link prediction task , such as neighborhood overlap , we introduce new metrics that model different types of interactions that take place between people . We study this claim empirically using data about URL forwarding activity on the social media sites Digg and Twitter . We show that structural proximity of two users in the follower graph is related to similarity of their activity , i.e. , how many URLs they both forward . We also show that given friends ' activity , knowing their proximity to the user can help better predict which URLs the user will forward . We compare the performance of different proximity metrics on the activity prediction task and find that metrics that take into account the attention-limited nature of interactions in social media lead to substantially better predictions .
doc_bio_tags: O B O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O B O O O B I O O O O O O O O O O O O O O O B I O O O O O O