An unsupervised learning approach to social circles detection in ego bluetooth proximity network

Abstract

Understanding a user's social interactions in the physical world proves important in building context-aware ubiquitous applications. A good way towards that objective is to categorize people to whom a user is socially related into what we call as social circles. In this note, we propose a novel unsupervised approach that learns from the Bluetooth (BT) sensed data recording one's dynamic proximity relations with others to identify her social circles, each of which is formed along a semantically coherent aspect. For each circle we learn its members as well as the temporal dimensions along which it is formed. Our method is innovative in that it well overcomes data sparsity by information sharing, and allows for circle overlaps which is common in reality. Experiments on real data demonstrate the effectiveness of our method, and also show the potentials of relational mobile data in sensing personal behaviors beyond personal data.

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