As people's friend lists grow longer, it becomes more and more difficult to manage a friend list by labeling or grouping friends manually. In this paper, we leverage on-board sensors of smart devices and propose a social activity mining framework Socialite, which is able to achieve social group discovering and friend auto-labeling by exploring users' interactions in physical word. Socialite considers different deployment strategies and mainly contains two stages: social activity recognition and social group detection. Together with several data analysis approaches, a voting based lightweight neural network is designed for high accuracy diverse activity recognition. Then we propose a novel algorithm for social interaction feature generation and measure correlation among features of even asynchronous social activities. For system evaluation, we conduct extensive real life experiments. Results demonstrate that Socialite can recognize diverse social activities with above 94% accuracy, and 100% accuracy with our voting scheme. Socialite can also detect social groups in different scenarios with high accuracy. For example in two people activities, our proposed method achieves 92.2% accuracy for walk and 92.6% accuracy for table tennis.
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