🕸️ Graph-based NLP system analyzing 10,000+ academic articles to uncover literature gaps with 0.73 correlation to organizational failures

Research-grade network analysis pipeline combining graph theory, NLP embeddings, and semantic similarity algorithms. Demonstrates how network topology in academic literature reveals critical research gaps with predictive power for real-world organizational outcomes.

Key Achievements:

• ✅ 10,000+ articles processed with NetworkX graph analysis
• ✅ 0.73 correlation between literature gaps and organizational failures (p < 0.05)
• ✅ 85% classification accuracy using embedding-based semantic similarity
• ✅ 5,115 connections mapped across 276 research concepts
• ✅ Community detection identifying 12 distinct research clusters

┌──────────────────────────────────────────────────────────────┐
│           Academic Literature Corpus (10K+ Articles)         │
└─────────────────────────┬────────────────────────────────────┘
                          │
                          ▼
┌──────────────────────────────────────────────────────────────┐
│              NLP Processing Pipeline                          │
│  • Text extraction & cleaning                                 │
│  • Keyword extraction (TF-IDF)                                │
│  • Semantic embedding generation                              │
└─────────────────────────┬────────────────────────────────────┘
                          │
                          ▼
┌──────────────────────────────────────────────────────────────┐
│              Network Graph Construction                       │
│  • Nodes: Research concepts (276 total)                       │
│  • Edges: Semantic similarity (5,115 connections)             │
│  • Weights: Co-occurrence frequency                           │
└─────────────────────────┬────────────────────────────────────┘
                          │
                          ▼
┌──────────────────────────────────────────────────────────────┐
│              Graph Analysis (NetworkX)                        │
│  • Centrality measures (degree, betweenness, eigenvector)     │
│  • Community detection (Louvain algorithm)                    │
│  • Literature gap identification                              │
└─────────────────────────┬────────────────────────────────────┘
                          │
                          ▼
┌──────────────────────────────────────────────────────────────┐
│              Predictive Analysis                              │
│  • Correlation with organizational failure data               │
│  • Classification model (85% accuracy)                        │
│  • Research opportunity ranking                               │
└──────────────────────────────────────────────────────────────┘

Technology Stack:

• Graph Analysis: NetworkX 3.0+
• NLP: spaCy, NLTK, sentence-transformers
• ML: Scikit-learn for classification
• Visualization: Matplotlib, Gephi export
• Data: Pandas, NumPy

0.73 correlation (p < 0.05) between semantic coverage gaps and organizational failure patterns demonstrates that:

• Underexplored research areas correlate with real-world failures
• Network topology predicts knowledge gaps
• Literature analysis has practical predictive power

• Accuracy: 85.3%
• Precision: 83.7%
• Recall: 86.1%
• F1-Score: 84.9%

Using embedding-based semantic similarity to classify concept relationships.

• TF-IDF scoring across 10K+ documents
• Minimum document frequency: 5
• Maximum document frequency: 0.8
• Top 500 keywords selected

• Sentence-BERT for contextual embeddings
• 768-dimensional vector space
• Cosine similarity for edge weights
• Threshold: 0.65 for connection

• Louvain algorithm for modularity optimization
• Resolution parameter: 1.0
• 12 communities identified
• Average modularity: 0.71

• Identified isolated nodes (potential gaps)
• Measured betweenness centrality (bridging concepts)
• Correlated with organizational failure dataset
• Statistical validation (p-value < 0.05)

pip install networkx pandas numpy scikit-learn spacy matplotlib
python -m spacy download en_core_web_sm

from network_analyzer import LiteratureNetworkAnalyzer

# Initialize analyzer
analyzer = LiteratureNetworkAnalyzer(corpus_path='data/articles/')

# Build network
analyzer.extract_keywords()
analyzer.build_network()

# Analyze
results = analyzer.detect_communities()
gaps = analyzer.identify_gaps()

# Visualize
analyzer.plot_network(output='network_graph.png')
analyzer.export_gephi(output='network.gexf')

• Keyword extraction: ~10 minutes
• Network construction: ~5 minutes
• Community detection: ~2 minutes
• Visualization: ~3 minutes

Advanced_Network_Intelligence/
├── data/
│   ├── articles/          # Input corpus
│   └── processed/         # Cleaned data
├── src/
│   ├── network_analyzer.py    # Main analysis class
│   ├── keyword_extractor.py   # TF-IDF processing
│   └── visualization.py       # Graph plotting
├── notebooks/
│   └── exploration.ipynb      # Exploratory analysis
├── results/
│   ├── network_graph.png
│   ├── communities.csv
│   └── gap_analysis.csv
├── README.md
└── requirements.txt

This research demonstrates:

• Novel application of graph theory to literature analysis
• Predictive modeling of knowledge gaps
• Validation of network topology as research metric
• Reproducible methodology for peer review

Potential Applications:

• Research funding prioritization
• Academic program development
• Literature review automation
• Cross-disciplinary opportunity detection

Interested in network analysis, NLP, or research gap detection?

• LinkedIn: linkedin.com/in/rosalinatorres
• Portfolio: rosalinatorres888.github.io
• Email: torres.ros@northeastern.edu

Part of my data engineering and ML/AI portfolio showcasing graph analysis, NLP, and research methodology