The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
Measuring and extracting proximity graphs in networks
ACM Transactions on Knowledge Discovery from Data (TKDD)
A trust model stemmed from the diffusion theory for opinion evaluation
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Predicting user activity level in social networks
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
Box office prediction based on microblog
Expert Systems with Applications: An International Journal
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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.