Shapely Hierarchical Graph Transformation
نویسنده
چکیده
Diagrams can be represented by graphs, and the animation and transformation of diagrams can be modeled by graph transformation. This paper studies extensions of graphs and graph transformation that are important for programming with graphs: We extend graphs by a notion of hierarchy that supports value composition, and define hierarchical graph transformation in an intuitive way that resembles term rewriting. We require that admissable shapes for hierarchical graphs are specified by context-free graph grammars, in order to set up a type discipline for shapely hierarchical graph transformation. The resulting computational model shall be the basis of the visual language DIAPLAN for programming with graphs that represent diagrams.
منابع مشابه
Abstraction and Control for Shapely Nested Graph Transformation
ion and Control for Shapely Nested Graph Transformation
متن کاملParallel Independence in Hierarchical Graph Transformation
Hierarchical graph transformation as defined in [1, 2] extends double-pushout graph transformation in the spirit of term rewriting: Graphs are provided with hierarchical structure, and transformation rules are equipped with graph variables. In this paper we analyze conditions under which diverging transformation steps H ⇐ G ⇒ H ′ can be joined by subsequent transformation sequences H ∗ ⇒ M ∗ ⇐ ...
متن کاملHierarchical Graph Transformation
When graph transformation is used for programming purposes, large graphs should be structured in order to be comprehensible. In this paper, we present an approach for the rule-based transformation of hierarchically structured hypergraphs. In these graphs, distinguished hyperedges contain graphs that can be hierarchical again. Our framework extends the well-known double-pushout approach from fla...
متن کاملGraph Clustering by Hierarchical Singular Value Decomposition with Selectable Range for Number of Clusters Members
Graphs have so many applications in real world problems. When we deal with huge volume of data, analyzing data is difficult or sometimes impossible. In big data problems, clustering data is a useful tool for data analysis. Singular value decomposition(SVD) is one of the best algorithms for clustering graph but we do not have any choice to select the number of clusters and the number of members ...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2001