In the past, quite a few fast algorithms have been developed to mine frequent patterns over graph data, with the large spectrum covering many variants of the problem. However, the real bottleneck for knowledge discovery on graphs is neither efficiency nor scalability, but the usability of patterns that are mined out. Currently, what the state-of-art techniques give is a lengthy list of exact patterns, which are undesirable in the following two aspects: (1) on the micro side, due to various inherent noises or data diversity, exact patterns are usually not too useful in many real applications; and (2) on the macro side, the rigid structural requirement being posed often generates an excessive amount of patterns that are only slightly different from each other, which easily overwhelm the users. In this paper, we study the presentation problem of graph patterns, where structural representatives are deemed as the key mechanism to make the whole strategy effective. As a solution to fill the usability gap, we adopt a two-step smoothing-clustering framework, with the first step adding error tolerance to individual patterns (the micro side), and the second step reducing output cardinality by collapsing multiple structurally similar patterns into one representative (the macro side). This novel, integrative approach is never tried in previous studies, which essentially rolls-up our attention to a more appropriate level that no longer looks into every minute detail. The above framework is general, which may apply under various settings and incorporate a lot of extensions. Empirical studies indicate that a compact group of informative delegates can be achieved on real datasets and the proposed algorithms are both efficient and scalable.