A class of Maximum A Posteriori(MAP) formulations built on various graph models are of great interests for both theoretical and practical applications. Recent advances in this field have extended the connections between the linear program (LP) relaxation and various tree-reweighted message passing algorithms. At both sides, many algorithms and their optimality certificates are proved, provided no conflict exists between the node marginal maximum and the corresponding edge marginal maximum. However, these conflicts are usually inevitable for general non-trivial Markov random fields (MRFs). Our work is aimed at reducing such conflicts by reparameterizing the original energy distributions in pairwise Markov random field. All node potentials will be decomposed and attached to local edges according to their local graph structures. And thus, only edge marginals are needed in our linear program relaxation, and the node marginals are only used to exchange information among different parts of the graph. We incorporated this consistent graph-structured reparameterization into some latest LP optimality guaranteed proximal solvers, and the resulted algorithms outperform the original ones in convergence rate and also have a better behavior to converge to MAP optimality monotonously even for some highly noisy MRFs.