Graph Pattern Mining (GPM) applications are memory intensive as they require a tremendous amount of edge checks. In recent years, the “set-centric” abstraction has gained attention for its powerful expressive abilities. By leveraging relational algebra, they optimized algorithms with methods like matching orders, early termination, automorphism-breaking, and result reuse to reduce redundancy.However, these approaches primarily address coarse-grained redundancy from exactly the same set formulas, neglecting that the data graph’s inherent locality may lead to fine-grained duplicated edge checks.In fact, even unrelated set operations may check the same pair of vertices. This paper introduces the missing set union operation to the set-centric abstraction to fuse duplicated edge checks into one. It maintains the expressive power of relational algebra and previous optimizations while effectively avoids fine-grained redundancy in GPM tasks.Compared to state-of-the-art methods, our method achieves significant speedup on a V100 GPU cluster, demonstrating up to 305 x faster performance than the state-of-the-art GPM system G^2 Miner