Abstract

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 …

Date

April 16, 2012

Authors

Kristina Lerman, Suradej Intagorn, Jeon-Hyung Kang, Rumi Ghosh

Book

Proceedings of the 21st international conference on World Wide Web

Pages

555-556