active library

cognitive-mri-conversations

Research compendium: Cognitive MRI of AI Conversations. Conference paper (Complex Networks 2025, Springer) and journal extension (PLOS Complex Systems).

Started 2025 Python

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Cognitive MRI of AI Conversations: Research Compendium

DOI

Applying complex network analysis to ChatGPT conversation archives to reveal knowledge organization, community structure, and temporal evolution patterns.

Authors: Alexander Towell and John Matta — Southern Illinois University Edwardsville

Overview

This repository transforms sequential AI conversation logs into semantic similarity networks, revealing latent cognitive structure in AI-assisted knowledge exploration. We analyze 1,908 ChatGPT conversations spanning December 2022 to April 2025, constructing networks that expose knowledge communities, bridge conversations, and temporal evolution patterns invisible in linear logs.

Papers

PaperVenueStatus
Temporal Evolution of Cognitive Knowledge Networks in AI-Assisted ConversationsPLOS Complex SystemsSubmitted
Cognitive MRI of AI Conversations: Analyzing AI Interactions through Semantic Embedding NetworksComplex Networks 2025 (Springer)Published

Repository Structure

├── comp-net-2025-journal/           # Journal extension (PLOS Complex Systems)
│   ├── paper/PLOS/                  # Submission-ready paper, figures, refs
│   └── code/                        # Temporal analysis scripts
├── comp-net-2025-camera-ready/      # Published conference paper (Springer)
│   ├── paper/                       # Camera-ready paper
│   └── slides/                      # Conference presentation (Beamer)
├── data/                            # Reproducibility data for both papers
│   ├── embeddings/                  # 1,908 conversation embeddings (2:1 ratio)
│   ├── network/                     # Primary edge list (601 nodes, 1,718 edges)
│   ├── temporal/                    # Journal paper: monthly network snapshots
│   ├── ablation/                    # Conference paper: 63-config parameter study
│   └── conversations/               # Placeholder (sanitization in progress)
└── code/                            # Embedding + network construction pipeline
    ├── cli.py                       # Main CLI (embeddings, edges, export)
    ├── networks.py                  # Network statistics & metrics
    ├── embedding/                   # LLM & TF-IDF embedding models
    ├── graph/                       # Edge generation, GPU acceleration
    └── run_ablation_study.py        # 63-config ablation study

Reproducing Results

All derived data needed to reproduce every figure and table in both papers is included in data/. See data/README.md for full documentation.

To regenerate all journal paper figures from the curated data:

pip install -r code/requirements.txt
bash data/reproduce.sh

This runs the temporal analysis pipeline (~42 seconds) using the embeddings and edge list in data/, producing all figures in comp-net-2025-journal/paper/figures/temporal/.

Building the Pipeline from Scratch

To regenerate embeddings and networks from raw conversations (requires Ollama with nomic-embed-text):

pip install -r code/requirements.txt

cd code
python cli.py node-embeddings --input-dir <conversations-dir> \
    --method role-aggregate --embedding-method llm --output-dir <output-dir>
python cli.py edges-gpu --input-dir <output-dir> --output-file edges.json
python cli.py cut-off --input-file edges.json --output-file filtered.json --cutoff 0.9
python cli.py export --nodes-dir <output-dir> --edges-file filtered.json --format gexf -o graph.gexf

The pipeline code is also available as a standalone package at chatgpt-complex-net (DOI: 10.5281/zenodo.15314235).

Citation

If you use this work, please cite the journal paper:

@article{towell2026temporal,
  author  = {Towell, Alexander and Matta, John},
  title   = {Temporal Evolution of Cognitive Knowledge Networks in AI-Assisted Conversations},
  journal = {PLOS Complex Systems},
  year    = {2026},
  note    = {Submitted}
}

See CITATION.cff for machine-readable citation metadata.

License

MIT

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