More on Graph Rewriting With Contextual Refinement

In GRGEN , a graph rewrite generator tool, rules have the outstandingfeature that variables in their pattern and replacement graphs may be refined withmeta-rules based on contextual hyperedge replacement grammars. A refined rule maydelete, copy, and transform subgraphs of unbounded size and of variable shape. Inthis paper, we show that rules with contextual refinement can be transformed to stan-dard graph rewrite rules that perform the refinement incrementally, and are appliedaccording to a strategy called residual rewriting. With this transformation, it is possi-ble to state precisely whether refinements can be determined in finitely many steps ornot, and whether refinements are unique for every form of refined pattern or not

Defining Models - Meta Models versus Graph Grammars

The precise specification of software models is a major concern in model-driven design of object-oriented software. Metamodelling and graph grammars are apparent choices for such specifications. Metamodelling has several advantages: it is easy to use, and provides procedures that check automatically whether a model is valid or not. However, it is less suited for proving properties of models, or for generating large sets of example models. Graph grammars, in contrast, offer a natural procedure - the derivation process - for generating example models, and they support proofs because they define a graph language inductively. However, not all graph grammars that allow to specify practically relevant models are easily parseable. In this paper, we propose contextual star grammars as a graph grammar approach that allows for simple parsing and that is powerful enough for specifying non-trivial software models. This is demonstrated by defining program graphs, a language-independent model of object-oriented programs, with a focus on shape (static structure) rather than behavior

Graph Rewriting with Contextual Refinement

In the standard theory of graph transformation, a rule modifies only subgraphs of constant size and fixed shape.  The rules supported by the graph-rewriting tool GrGen are far more expressive: they may modify subgraphs of unbounded size and variable shape. Therefore properties like termination and confluence cannot be analyzed as for the standard case. In order to lift such results, we formalize the outstanding feature of GrGen rules by using plain rules on two levels: schemata} are rules with variables; they are refined with meta-rules, which are based on contextual hyperedge replacement, before they are used for rewriting.We show that every rule based on single pushouts, on neighborhood-controlled embedding, or on variable substitution can be modeled by a schema with appropriate meta-rules. It turns out that the question whether schemata may have overlapping refinements is not decidable

Generating Semantic Graph Corpora with Graph Expansion Grammar

We introduce Lovelace, a tool for creating corpora of semantic graphs. The system uses graph expansion grammar as a representational language, thus allowing users to craft a grammar that describes a corpus with desired properties. When given such grammar as input, the system generates a set of output graphs that are well-formed according to the grammar, i.e., a graph bank. The generation process can be controlled via a number of configurable parameters that allow the user to, for example, specify a range of desired output graph sizes. Central use cases are the creation of synthetic data to augment existing corpora, and as a pedagogical tool for teaching formal language theory.Comment: In Proceedings NCMA 2023, arXiv:2309.0733

Equivariant Hypergraph Diffusion Neural Operators

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction tasks built upon such higher-order relations. However, higher-order relations in practice contain complex patterns and are often highly irregular. So, it is often challenging to design an HNN that suffices to express those relations while keeping computational efficiency. Inspired by hypergraph diffusion algorithms, this work proposes a new HNN architecture named ED-HNN, which provably represents any continuous equivariant hypergraph diffusion operators that can model a wide range of higher-order relations. ED-HNN can be implemented efficiently by combining star expansions of hypergraphs with standard message passing neural networks. ED-HNN further shows great superiority in processing heterophilic hypergraphs and constructing deep models. We evaluate ED-HNN for node classification on nine real-world hypergraph datasets. ED-HNN uniformly outperforms the best baselines over these nine datasets and achieves more than 2\%$\uparrow$ in prediction accuracy over four datasets therein.Comment: Code: https://github.com/Graph-COM/ED-HN

Towards rule-based visual programming of generic visual systems

This paper illustrates how the diagram programming language DiaPlan can be used to program visual systems. DiaPlan is a visual rule-based language that is founded on the computational model of graph transformation. The language supports object-oriented programming since its graphs are hierarchically structured. Typing allows the shape of these graphs to be specified recursively in order to increase program security. Thanks to its genericity, DiaPlan allows to implement systems that represent and manipulate data in arbitrary diagram notations. The environment for the language exploits the diagram editor generator DiaGen for providing genericity, and for implementing its user interface and type checker.Comment: 15 pages, 16 figures contribution to the First International Workshop on Rule-Based Programming (RULE'2000), September 19, 2000, Montreal, Canad

Parsing of Adaptive Star Grammars

In a recent paper, adaptive star grammars have been proposed as an extension of node and hyperedge replacement grammars. A rule in an adaptive star grammar is actually a rule schema which, via the so-called cloning operation, yields a potentially infinite number of concrete rules. Adaptive star grammars are motivated by application areas such as modeling and refactoring object-oriented programs, and they are more powerful than node and hyperedge replacement grammars by this mechanism. It has been shown that the membership problem is decidable for a reasonably large subclass of adaptive star grammars, however no parser has been proposed. This paper describes such a parser for this subclass motivated by the well-known string parser by Cocke, Younger, and Kasami

COSMICAH 2005: workshop on verification of COncurrent Systems with dynaMIC Allocated Heaps (a Satellite event of ICALP 2005) - Informal Proceedings

Lisboa Portugal, 10 July 200