Validierung relationaler graphischer neuronaler Netze mit Attributen und Richtlinien

**What you’re seeing:**

These visualizations showcase the application of **Relational Graph Neural Networks (RGNNs)** to visualize complex relationships within legal documents, amendments, and court precedents. Each node in the graph represents a legal section, amendment, or court case, while the edges (lines) show how they relate to each other through citations, amendments, and references.

Key aspects of the visualization:

• **Nodes**: Represent sections of the law (e.g., “Section 61”), amendments (e.g., “Tax Cuts and Jobs Act”), or legal precedents (e.g., “Precedent 1 (2001)”).

• **Edges**: The connections between nodes represent relationships, such as amendments, citations, or references between legal texts and court rulings.

• **Color Coding**: Different types of legal entities are color-coded. For example, amendments are in red, legal sections in green, and precedents or court cases in blue.

**How to interpret it:**

• You can see how one law or amendment might change another, and how court precedents (represented by blue nodes) interpret these sections differently over time.

• The zoom and pan feature allows exploration of the data at multiple levels, and clicking on a node reveals more detailed information like its legal status and jurisdiction.

The intent behind this is to help visualize how laws evolve, how amendments build upon or conflict with existing sections, and how court precedents influence legal interpretation. By structuring legal relationships as a network, we can gain insights into legal complexities that might otherwise be difficult to grasp.

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