document: Modeling relationship strength in online social networks Previous work analyzing social networks has mainly focused on binary friendship relations . However , in online social networks the low cost of link formation can lead to networks with heterogeneous relationship strengths ( e.g. , acquaintances and best friends mixed together ) . In this case , the binary friendship indicator provides only a coarse representation of relationship information . In this work , we develop an unsupervised model to estimate relationship strength from interaction activity ( e.g. , communication , tagging ) and user similarity . More specifically , we formulate a link-based latent variable model , along with a coordinate ascent optimization procedure for the inference . We evaluate our approach on real-world data from Facebook and LinkedIn , showing that the estimated link weights result in higher autocorrelation and lead to improved classification accuracy .
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