Biological network alignment benefits the evolutionary and comparative biology by providing regions of topological and functional similarity between different species. However, most existing network aligners follow heuristic methods and only capture the static information that based purely on the original isolated networks, while there also exists valuable interactive information hidden in the resulted alignment that provides additional signals for further improvement. In this paper, we propose an iterative method IMAP to improve the quality of existing network aligners. IMAP starts from an imperfect seed alignment generated by any aligner, and then iteratively refines it by capturing interactive information hidden in current alignment until convergence. Within each iteration, we calculate the likelihood of pairwise alignment using supervised learning techniques, hence heuristic functions are no longer required. Furthermore, we extend IMAP to start from multiple seed aligners to combine their individual advantages. Comprehensive experiments indicate that IMAP improves existing network aligners significantly in terms of node correctness, topology conservation and biological similarity. Therefore, IMAP can benefit the subsequent cross-species biology researches by providing high-quality alignment between PPI networks.