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Network alignment aims to identify the corresponding nodes belonging to the same entity across different networks, which is a fundamental task in various applications. Existing embedding-based approaches usually involve two stages, namely embedding and matching. The embedding stage conducts network embedding on each network to capture the primary structural regularity. In the matching stage, a mapping function is built to project the learned embeddings to the same latent space. However, these approaches typically encounter two challenges: (1) the difficulty of unifying the elusive embedding spaces to the same latent space; (2) the difficulty of distinguishing the real anchor nodes from their neighbors, resulting in the confounding matching problem. To address these challenges, we present the Collaborative Cross-Network Embedding (CCNE) framework in this paper. This framework provides a …