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Charting the Design Space of Neural Graph Representations for Subgraph Matching Vaibhav Raj ⋅ Indradyumna Roy ⋅ Ashwin Ramachandran ⋅ Soumen Chakrabarti ⋅ Abir De Subgraph matching is vital in knowledge graph (KG) question answering, molecule design, scene graph, code and circuit search, etc in average reward and $71 , cascades modality-specific models, each responsible for generating an intermediate representation, such as contours, palettes, and detailed textures, ultimately culminating in a high-quality RGB image achieves notable efficiency, matching LDM performance on LSUN-Churches while operating 2$\times$ faster with a 3$\times$ smaller architecture $, by generalizing beyond translational symmetry