The availability of an overwhelmingly large amount of bibliographic information including citation and co-authorship data makes it imperative to have a systematic approach that will enable an author to organize her own personal academic network profitably. An effective method could be to have one’s co-authorship network arranged into a set of “circles”, which has been a recent practice for organizing relationships (e.g., friendship) in many online social networks.

An unsupervised approach is proposed to automatically detect circles in an ego network such that each circle represents a densely knit community of researchers. The model uses an unsupervised method which combines a variety of node features and node similarity measures.

The model is built from a rich co-authorship network data of more than 8 hundred thousand authors. Experimental results show that including the circle information detected by our model improves link prediction performance of a number of state of the art link prediction mechanisms including Supervised Random Walks.