Automatic Social Circle Detection Using Multi-View Clustering

With the development of information technology, online social networks grow dramatically. They now play a significant role in people's social life, especially for the younger generation. While huge amount of information is available in online social networks, privacy concerns arise. Among various privacy protection proposals, the notions of privacy as control and information boundary have been introduced. Commercial social networking sites have adopted the concept to implement mechanisms such as Google circles and Facebook custom lists. However, the functions are not widely accepted by the users, partly because it is tedious and labor-intensive to manually assign friends into circles.

In this paper, we introduce a social circle discovery approach using multi-view clustering. First, we present our observations on the key features of social circles: friendship links, content similarity and social interactions. We propose a one-side co-trained spectral clustering algorithm, which is tailored for the sparse nature of social network data. We also propose two evaluation measurements. One is based on quantitative similarity measures, while the other employs human evaluators to examine pairs of users selected by the max-risk evaluation approach. We evaluate our approach on ego networks of twitter users, and compare the proposed technique with single-view clustering and original co-trained spectral clustering techniques. Results show that multi-view clustering is more accurate for social circle detection; and our proposed approach gains significantly higher similarity ratio than the original multi-view clustering approach.