Problem Statement

The cargo examples for graph and statistics are complete (issue #10), but the book documentation is missing:

Missing Components:

• ❌ book/src/ml-fundamentals/graph-algorithms.md theory chapter
• ❌ book/src/ml-fundamentals/descriptive-statistics.md theory chapter
• ❌ book/src/examples/graph-social-network.md case study
• ❌ book/src/examples/descriptive-statistics.md case study
• ❌ SUMMARY.md not updated with new chapters

Impact: Users cannot learn the theory behind the graph and statistics implementations.

Proposed Solution

Add comprehensive book chapters following the established pattern from other ML fundamentals.

Book Chapters to Create

1. book/src/ml-fundamentals/graph-algorithms.md (~400-600 lines)

   • Graph representation (CSR vs adjacency list)
   • Degree centrality theory (Freeman normalization)
   • PageRank theory (power iteration, Kahan summation, dangling nodes)
   • Betweenness centrality (Brandes algorithm, parallel implementation)
   • Real-world applications
   • Code examples with explanations

2. book/src/ml-fundamentals/descriptive-statistics.md (~400-600 lines)

   • Quantile methods (R-7 interpolation)
   • Five-number summary and IQR
   • Histogram binning strategies (FreedmanDiaconis, Sturges, Scott)
   • QuickSelect optimization
   • Use cases (EDA, outlier detection)
   • Code examples with explanations

3. book/src/examples/graph-social-network.md (~300-400 lines)

   • Full walkthrough of social network example
   • Interpretation of centrality scores
   • Performance notes (CSR benefits, parallel betweenness)
   • Real-world insights

4. book/src/examples/descriptive-statistics.md (~300-400 lines)

   • Full walkthrough of statistics example
   • Histogram method selection guidance
   • When to use each binning strategy
   • Outlier detection walkthrough

5. Update book/src/SUMMARY.md

   • Add to "Machine Learning Fundamentals" section
   • Add to "Real-World Examples" section

Technical Requirements

Book Chapters:

• Theory sections with mathematical notation
• Code blocks must be tested (mdbook test)
• Real-world applications highlighted
• Links to API documentation
• References to peer-reviewed papers (from spec)
• Follow existing chapter structure

Success Criteria

• ✅ 4 new book chapters written
• ✅ SUMMARY.md updated with new chapters
• ✅ All code blocks pass mdbook test
• ✅ Consistent style with existing chapters
• ✅ Zero clippy warnings
• ✅ Clear, educational content

References

• Existing examples: cargo run --example graph_social_network, cargo run --example descriptive_statistics
• Existing book chapters: book/src/ml-fundamentals/linear-regression.md, book/src/examples/kmeans-clustering.md
• Implementation: src/graph/mod.rs, src/stats/mod.rs
• Specification: docs/specifications/graph-traditional-descriptive-statistics-spec.md v1.1.0

Acceptance Criteria

• book/src/ml-fundamentals/graph-algorithms.md written
• book/src/ml-fundamentals/descriptive-statistics.md written
• book/src/examples/graph-social-network.md written
• book/src/examples/descriptive-statistics.md written
• book/src/SUMMARY.md updated
• mdbook test passes
• Zero clippy warnings