06a42cc746
- Add CLAUDE.md with project guidance for Claude Code - Add LaTeX/ with paper and figure generation scripts - Remove papers/ directory (replaced by LaTeX/) Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
136 lines
5.1 KiB
Markdown
136 lines
5.1 KiB
Markdown
# Figure Generation for Copycat Graph Theory Paper
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This folder contains Python scripts to generate all figures for the paper "From Hardcoded Heuristics to Graph-Theoretical Constructs."
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## Prerequisites
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Install Python 3.7+ and required packages:
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```bash
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pip install matplotlib numpy networkx scipy
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```
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## Quick Start
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Generate all figures at once:
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```bash
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python generate_all_figures.py
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```
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Or run individual scripts:
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```bash
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python generate_slipnet_graph.py # Figure 1: Slipnet graph structure
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python activation_spreading.py # Figure 2: Activation spreading dynamics
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python resistance_distance.py # Figure 3: Resistance distance heat map
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python workspace_evolution.py # Figures 4 & 5: Workspace evolution & betweenness
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python clustering_analysis.py # Figure 6: Clustering coefficient analysis
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python compare_formulas.py # Comparison plots of formulas
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```
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## Generated Files
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After running the scripts, you'll get these figures:
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### Main Paper Figures
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- `figure1_slipnet_graph.pdf/.png` - Slipnet graph with conceptual depth gradient
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- `figure2_activation_spreading.pdf/.png` - Activation spreading over time with differential decay
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- `figure3_resistance_distance.pdf/.png` - Resistance distance vs shortest path comparison
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- `figure4_workspace_evolution.pdf/.png` - Workspace graph at 4 time steps
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- `figure5_betweenness_dynamics.pdf/.png` - Betweenness centrality over time
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- `figure6_clustering_distribution.pdf/.png` - Clustering coefficient distributions
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### Additional Comparison Plots
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- `formula_comparison.pdf/.png` - 6-panel comparison of all hardcoded formulas vs proposed alternatives
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- `scalability_comparison.pdf/.png` - Performance across string lengths and domain transfer
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- `slippability_temperature.pdf/.png` - Temperature-dependent slippability curves
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- `external_strength_comparison.pdf/.png` - Current support factor vs clustering coefficient
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## Using Figures in LaTeX
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Replace the placeholder `\fbox` commands in `paper.tex` with:
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```latex
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\begin{figure}[htbp]
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\centering
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\includegraphics[width=0.8\textwidth]{figure1_slipnet_graph.pdf}
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\caption{Slipnet graph structure...}
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\label{fig:slipnet}
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\end{figure}
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```
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## Script Descriptions
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### 1. `generate_slipnet_graph.py`
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Creates a visualization of the Slipnet semantic network with 30+ key nodes:
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- Node colors represent conceptual depth (blue=concrete, red=abstract)
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- Edge thickness shows link strength (inverse of link length)
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- Hierarchical layout based on depth values
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### 2. `compare_formulas.py`
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Generates comprehensive comparisons showing:
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- Support factor: 0.6^(1/n³) vs clustering coefficient
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- Member compatibility: Discrete (0.7/1.0) vs continuous structural equivalence
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- Group length factors: Step function vs subgraph density
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- Salience weights: Fixed (0.2/0.8) vs betweenness centrality
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- Activation jump: Fixed threshold (55.0) vs adaptive percolation threshold
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- Mapping factors: Linear increments vs logarithmic path multiplicity
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Also creates scalability analysis showing performance across problem sizes and domain transfer.
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### 3. `activation_spreading.py`
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Simulates Slipnet activation dynamics with:
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- 3 time-step snapshots showing spreading from "sameness" node
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- Heat map visualization of activation levels
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- Time series plots demonstrating differential decay rates
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- Annotations showing how shallow nodes (letters) decay faster than deep nodes (abstract concepts)
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### 4. `resistance_distance.py`
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Computes and visualizes resistance distances:
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- Heat map matrix showing resistance distance between all concept pairs
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- Comparison with shortest path distances
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- Temperature-dependent slippability curves for key concept pairs
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- Demonstrates how resistance distance accounts for multiple paths
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### 5. `clustering_analysis.py`
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Analyzes correlation between clustering and success:
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- Histogram comparison: successful vs failed runs
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- Box plots with statistical tests (t-test, p-values)
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- Scatter plot: clustering coefficient vs solution quality
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- Comparison of current support factor formula vs clustering coefficient
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### 6. `workspace_evolution.py`
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Visualizes dynamic graph rewriting:
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- 4 snapshots of workspace evolution for abc→abd problem
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- Shows bonds (blue edges), correspondences (green dashed edges)
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- Annotates nodes with betweenness centrality values
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- Time series showing how betweenness predicts correspondence selection
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## Customization
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Each script can be modified to:
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- Change colors, sizes, layouts
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- Add more nodes/edges to graphs
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- Adjust simulation parameters
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- Generate different problem examples
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- Export in different formats (PDF, PNG, SVG)
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## Troubleshooting
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**"Module not found" errors:**
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```bash
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pip install --upgrade matplotlib numpy networkx scipy
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```
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**Font warnings:**
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These are harmless warnings about missing fonts. Figures will still generate correctly.
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**Layout issues:**
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If graph layouts look cluttered, adjust the `k` parameter in `nx.spring_layout()` or use different layout algorithms (`nx.kamada_kawai_layout()`, `nx.spectral_layout()`).
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## Contact
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For questions about the figures or to report issues, please refer to the paper:
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"From Hardcoded Heuristics to Graph-Theoretical Constructs: A Principled Reformulation of the Copycat Architecture"
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