Code clone detection refers to finding the functionally similarities between two code fragments, which is becoming increasingly important with the evolution of the software engineering field. There are numbers of code clone detection methods have been proposed, including tree-based methods that are capable of detecting semantic code clones. However, due to the fact that tree structure is complex, these methods are difficult to apply to large-scale clone detection. In this paper, we propose a scalable semantic code clone detector based on semantically enhanced abstract syntax tree. Specifically, we add the control flow and data flow details into the original tree and regard the enhanced tree as a social network. Then we build a social network-based triads model to collect the similarity features between the two methods by analyzing different types of triads within the network. After obtaining all features, we use them to train a machine learning-based semantic code clone detector (\ie Tritor). To examine the capability of Tritor, we evaluate it on two widely used datasets with other nine state-of-the-art code clone detection systems. The experimental results show that Tritor has better detection performance than SourcererCC, RtvNN, Deckard, ASTNN, TBCNN, CDLH, SCDetector, DeepSim, and FCCA. As for scalability, Tritor is about 39 times faster than another current state-of-the-art tree-based code clone detector ASTNN.