Legalese-Node-LN1

Precedent Graph Models

The Precedent Graph system models legal relationships and citations as a directed graph, enabling sophisticated legal analysis and precedent tracking within the LN1 node network.

Graph Structure

Core Components

class PrecedentGraph:
    def __init__(self):
        self.nodes = {}  # Legal documents
        self.edges = {}  # Citations and relationships
        self.metadata = MetadataManager()
        self.analyzer = GraphAnalyzer()

Node Properties

interface DocumentNode {
    id: string;
    metadata: {
        title: string;
        jurisdiction: string;
        date: timestamp;
        court: string;
        status: string;
    };
    relationships: {
        citations: string[];
        references: string[];
        overruled: string[];
    }
}

Graph Operations

Citation Analysis

class CitationAnalyzer:
    def analyze_citations(self, document):
        return {
            'forward_citations': self.get_citing_documents(),
            'backward_citations': self.get_cited_documents(),
            'citation_strength': self.calculate_importance(),
            'citation_context': self.extract_context()
        }

Relationship Mapping

• Direct citations
• Indirect references
• Overruling relationships
• Related cases

Graph Algorithms

Path Analysis

class PathAnalyzer:
    def analyze_precedent_path(self, start_node, end_node):
        return {
            'shortest_path': self.find_shortest_path(),
            'all_paths': self.find_all_paths(),
            'path_strength': self.calculate_path_strength(),
            'key_intermediates': self.identify_key_nodes()
        }

Centrality Metrics

• PageRank implementation
• Citation authority
• Legal influence scoring
• Node importance

Visualization

Graph Rendering

class GraphVisualizer:
    def generate_visualization(self, graph_subset):
        return {
            'layout': self.calculate_layout(),
            'node_styling': self.style_nodes(),
            'edge_styling': self.style_edges(),
            'interactive_elements': self.add_interactivity()
        }

Interactive Features

• Zoom and pan
• Node filtering
• Path highlighting
• Relationship exploration

Data Integration

Document Processing

class DocumentProcessor:
    def process_document(self, document):
        return {
            'extracted_citations': self.extract_citations(),
            'relationship_mapping': self.map_relationships(),
            'context_analysis': self.analyze_context(),
            'metadata_enrichment': self.enrich_metadata()
        }

Update Mechanisms

• Real-time updates
• Batch processing
• Incremental changes
• Version control

Analysis Tools

Legal Research

class ResearchTools:
    def analyze_precedent(self, case_id):
        return {
            'authority_score': self.calculate_authority(),
            'citation_network': self.map_network(),
            'temporal_analysis': self.analyze_timeline(),
            'jurisdiction_mapping': self.map_jurisdictions()
        }

Impact Analysis

• Citation frequency
• Authority metrics
• Temporal relevance
• Jurisdiction spread

Performance Optimization

Graph Operations

class GraphOptimizer:
    def optimize_operations(self):
        return {
            'index_optimization': self.optimize_indices(),
            'query_caching': self.implement_cache(),
            'batch_processing': self.optimize_batches(),
            'memory_management': self.manage_memory()
        }

Scaling Considerations

• Distributed processing
• Partition strategies
• Cache management
• Query optimization

Integration APIs

Query Interface

class GraphAPI:
    def query_endpoints(self):
        return {
            '/graph/search': self.handle_search,
            '/graph/analyze': self.handle_analysis,
            '/graph/visualize': self.handle_visualization,
            '/graph/update': self.handle_updates
        }

Monitoring and Metrics

Performance Tracking

class GraphMonitor:
    def track_metrics(self):
        return {
            'query_performance': self.measure_query_speed(),
            'graph_size': self.measure_graph_size(),
            'update_frequency': self.track_updates(),
            'usage_patterns': self.analyze_usage()
        }

Remember to update this documentation as new features and improvements are implemented in the precedent graph system.

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