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. Categories and Subject Descriptors H.4.m [Information Systems]: Miscellaneous General Terms Algorithms, Design Keyword...

• Type: Conference
• Year: 2010
• Where: WWW
• Authors: Rongjing Xiang, Jennifer Neville, Monica Rogati
• Keywords: Binary Friendship, Internet Technology, Latent Variable Models, Social Networks, WWW 2010