In this paper, we introduce the concept of -orthogonal patterns to mine a representative set of graph patterns. Intuitively, two graph patterns are -orthogonal if their similarity is bounded above by . Each -orthogonal pattern is also a representative for those patterns that are at least similar to it. Given user defined , [0, 1], the goal is to mine an -orthogonal, -representative set that minimizes the set of unrepresented patterns. We present ORIGAMI, an effective algorithm for mining the set of representative orthogonal patterns. ORIGAMI first uses a randomized algorithm to randomly traverse the pattern space, seeking previously unexplored regions, to return a set of maximal patterns. ORIGAMI then extracts an orthogonal, -representative set from the mined maximal patterns. We show the effectiveness of our algorithm on a number of real and synthetic datasets. In particular, we show that our method is able to extract high quality patterns even in cases where existing enumerative graph mining methods fail to do so.