Learning random walks to rank nodes in graphs
Proceedings of the 24th international conference on Machine learning
Measurement and analysis of online social networks
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Mixed Membership Stochastic Blockmodels
The Journal of Machine Learning Research
User interactions in social networks and their implications
Proceedings of the 4th ACM European conference on Computer systems
Online social networks in economics
Decision Support Systems
Patterns and dynamics of users' behavior and interaction: Network analysis of an online community
Journal of the American Society for Information Science and Technology
Power-Law Distributions in Empirical Data
SIAM Review
You are who you know: inferring user profiles in online social networks
Proceedings of the third ACM international conference on Web search and data mining
Supervised random walks: predicting and recommending links in social networks
Proceedings of the fourth ACM international conference on Web search and data mining
Walking on a graph with a magnifying glass: stratified sampling via weighted random walks
ACM SIGMETRICS Performance Evaluation Review - Performance evaluation review
Semi-supervised ranking on very large graphs with rich metadata
Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining
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This paper investigates how local preferences and social structural constraints might affect the development of the friendship network in Facebook. We do this by analyzing a dataset of an American university, Caltech, and by building an agent-based simulation for comparison. Several different, but plausible, processes of friendship network development are proposed in which the structural information of the growing network and the student preferences are taken into account. `Network formation based on personal preference and social structure' matches the data best, and is thus the preferred hypothesis for the way that students add "friends" on Facebook.