also fundamental in verification approaches based on separation logic [@cr:relational-inductive-shape-analysis; @cdhy:compositional-shape-analysis; @lrc:shape-analysis-unstructured-sharing], where it is also known as rearrangement [@h:primer-separation-logic], a special case of frame inference. Shape analysis—construed in a wide sense—has been adapted to graph transformation [@r:gra-handbook], a general purpose modelling language for systems with dynamically evolving topology, such as network protocols and cyber-physical systems. Motivated by earlier work of shape analysis for graph transformation [@sww:abstract-gts; @bw:partner-abstraction; @b:cluster-abstraction-thesis; @br:cluster-abstraction; @rz:neighbourhood-abstraction-groove; @r:canonical-graph-shapes], we want to put the materialization operation on a new footing, widening the scope of shape analysis. A natural abstraction mechanism for transition systems with graphs as states “summarizes” all graphs over a specific *shape graph*. Thus a single graph is used as abstraction for all graphs that can be mapped homomorphically into it. Further annotations on shape graphs, such as cardinalities of preimages of its nodes and general first-order formulas, enable fine-tuning of the granularity of abstractions. While these natural abstraction principles have been successfully applied in previous work [@sww:abstract-gts; @bw:partner-abstraction; @b:cluster-abstraction-thesis; @br:cluster-abstraction; @rz:neighbourhood-abstraction-groove; @r:canonical-graph-shapes], their companion materialization constructions are notoriously difficult to develop, hard to understand, and are redrawn from scratch for every single setting. Thus, we set out to explain materializations based on mathematical principles, 