Network alignment aims at finding a bijective mapping between nodes of two networks. Due to its wide application in various fields (e.g., Computer Vision, Data Management, Bioinformatics, and Privacy Protection), researchers have proposed many network alignment algorithms, most of which rely on a set of pre-mapped seeds. However, it is challenging to identify an initial credible set of seeds solely with structural information. In this paper, by exploiting the observation that a true mapping leads to a large portion of consistent edges among the mapped nodes, we formally define the credibility of a mapping as its deviation from a random one. This enables us to measure the credibility of an initial set of seeds. We also present DeepMatching which is a seed identification framework for social network alignment. First, we represent the nodes of the two mapping networks with their structural feature vectors by employing graph embedding techniques. Second, we obtain an initial mapping of the nodes based on the obtained vectors by leveraging point set registration methods. Third, we develop a heuristic algorithm to extract a credible set of seed from the initial mapping. Finally, we utilize the extracted seed set as input of an efficient propagation-based algorithm for large scale network alignment. We conduct extensive experiments to evaluate the performance of DeepMatching, and the results clearly demonstrate its effectiveness and the efficiency.