In this paper, we address the issue for learning better translation consensus in machine translation (MT) research, and explore the search of translation consensus from similar, rather than the same, source sentences or their spans. Unlike previous work on this topic, we formulate the problem as structured labeling over a much smaller graph, and we propose a novel structured label propagation for the task. We convert such graph-based translation consensus from similar source strings into useful features both for n-best output re-ranking and for decoding algorithm. Experimental results show that, our method can significantly improve machine translation performance on both IWSLT and NIST data, compared with a state-of-the-art baseline.