Stochastic models can be found in various domains. For example, biochemical processes such as molecular interactions or the dynamics of wireless network topologies, where changes occur with certain probabilities. Having the ability to simulate scenarios in these domains can be crucial when real-life observations of certain processes are infeasible, e.g., protein-protein interactions in biochemistry, or expensive, e.g., building large wireless networks for research purposes. Stochastic graph transformation systems provide the means to describe the structure and simulate the behavior of such probability-driven environments in an adequate way, by modelling the state transitions using graph transformation rules, whose application depends on the current state and their application probabilities. To the best of our knowledge, there is currently no general-purpose simulation tool available anymore that performs rule-based simulations using stochastic graph transformation. Therefore, we developed SimSG a modular stochastic simulation tool that addresses the needs of a wide range of application domains-in contrast to most specialized simulation tools that are limited to one domain only. To facilitate the versatility of the tool, SimSG can be configured to employ different general-purpose tools for incremental graph pattern matching (currently, Democles and Viatra). We evaluate SimSG based on two use cases: First, using an example of the biochemistry domain, we conduct a comparative evaluation against the domain-specific tool KaSim. Second, we underpin the general-purpose applicability of SimSG by analyzing the simulation of a wireless sensor network scenario.
In the Model-Driven Software Engineering (MDSE) community, the combination of techniques operating on graph-based models (e.g., Pattern Matching (PM) and Graph Transformation (GT)) and Integer Linear Programming (ILP) is a common occurrence, since ILP solvers offer a powerful approach to solve linear optimization problems and help to enforce global constraints while delivering optimal solutions. However, designing and specifying complex optimization problems from more abstract problem descriptions can be a challenging task. A designer must be an expert in the specific problem domain as well as the ILP optimization domain to translate the given problem into a valid ILP problem. Typically, domain-specific ILP problem generators are hand-crafted by experts, to avoid specifying a new ILP problem by hand for each new instance of a problem domain. Unfortunately, the task of writing ILP problem generators is an exercise, which has to be repeated for each new scenario, tool, and approach. For this purpose, we introduce the GIPS (Graph-Based ILP Problem Specification Tool) framework 1 that simplifies the development of ILP problem generators for graph-based optimization problems and a new Domain-Specific Language (DSL) called GIPSL (Graph-Based ILP Problem Specification Language) that integrates GT and ILP problems on an abstract level. Our approach uses GIPSL specifications as a starting point to derive ILP problem generators for a specific application domain automatically. First experiments show that the derived ILP problem generators can compete with hand-crafted programs developed by ILP experts.
Adaptive networks model social, physical, technical, or biological systems as attributed graphs evolving at the level of both their topology and data. They are naturally described by graph transformation, but the majority of authors take an approach inspired by the physical sciences, combining an informal description of the operations with programmed simulations, and systems of ODEs as the only abstract mathematical description. We show that we can capture a range of social network models, the so-called voter models, as stochastic attributed graph transformation systems, demonstrate the benefits of this representation and establish its relation to the non-standard probabilistic view adopted in the literature. We use the theory and tools of graph transformation to analyze and simulate the models and propose a new variant of a standard stochastic simulation algorithm to recreate the results observed.
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