Many classification tasks can be carried out by casting a domain-specific problem to a general graph representation (with objects to be organized as graph nodes and pairwise similarities as graph edges) followed by graph partition. In this paper, an adaptation scheme is proposed to integrate multiple graphs from various cues to a single graph, such that the distance between the ideal transition probability matrix to the one derived from cue integration is minimized. Four different distance measures, i.e., the Frobenius norm, the Kullback-Leibler directed divergence, the Jeffrey divergence and the cross entropy, are investigated to minimize the discrepancy. It is shown that the minimization leads to a closed-form nonlinear optimization that can be solved by the Levenberg-Marguardt method. Domain- and task-specific knowledge is explored to facilitate the generic pattern classification task. Experimental results are demonstrated for image content description by multiple cue integration.