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Understanding how people communicate with one another plays a very important role in many disciplines including social psychology, economics, marketing, and management science. This paper proposes and evaluates SocialWeaver, a sensing service running on smartphones that performs conversation clustering and builds conversation networks automatically. SocialWeaver uses a hybrid speaker classification scheme that exploits an adaptive histogram-based classifier to non-obtrusively bootstrap the in situ speaker model learning. The conversation clustering algorithm proposed is able to detect fine-grain conversation groups even if speakers are close together. Finally, to address energy constrain, a POMDP-based energy control scheme is incorporated. We evaluate the performance of each component in SocialWeaver using more than 100 hours of conversation data collected from conversation groups with sizes ranging from 2 to 13. Evaluation shows that accuracy of 71% to 92% can be achieved for various conversation modes and up to 50% of the energy consumption in SocialWeaver can be reduced through the POMDP-based scheme. Evaluations of SocialWeaver in both controlled and uncontrolled settings show promising results in realistic settings and potential to enable many future applications.