Scientific Citation Network and Knowledge Mapper#

When to Use This Skill#

• identifying seminal papers in a research field
• mapping research lineage and intellectual heritage
• discovering related work through reference tracking
• finding potential collaborators through co-citation analysis
• tracking citation patterns to identify research trends
• building literature reviews with comprehensive coverage

Quick Start#

python

from scripts.main import CitationChasingMapping

# Initialize the tool
tool = CitationChasingMapping()

from scripts.citation_mapper import CitationNetworkMapper

mapper = CitationNetworkMapper(data_source="PubMed")

# Build citation network from seed paper
network = mapper.build_network(
    seed_paper={
        "pmid": "12345678",
        "title": "Breakthrough Discovery in Immunotherapy"
    },
    backward_depth=2,  # references of references
    forward_depth=2,   # citing papers of citing papers
    max_papers=500
)

# Identify seminal papers
seminal_papers = mapper.identify_seminal_works(
    network=network,
    min_citations=100,
    centrality_threshold=0.8
)

print(f"Found {len(seminal_papers)} highly influential papers:")
for paper in seminal_papers[:5]:
    print(f"  - {paper.title} (cited {paper.citation_count} times)")

# Find research clusters
clusters = mapper.identify_research_clusters(
    network=network,
    algorithm="louvain",
    min_cluster_size=10
)

# Generate collaboration map
collaboration_map = mapper.generate_collaboration_network(
    network=network,
    institution_field="affiliation"
)

# Create visualization
mapper.visualize_network(
    network=network,
    layout="force_directed",
    color_by="publication_year",
    size_by="citation_count",
    output_file="citation_network.pdf"
)

Core Capabilities#

1. Build Comprehensive Citation Networks#

Construct bidirectional citation graphs from seed papers with configurable depth.

python

# Build network from multiple seed papers
network = mapper.build_network(
    seed_papers=[
        {"pmid": "12345678", "title": "Original Discovery"},
        {"pmid": "87654321", "title": "Follow-up Study"}
    ],
    backward_depth=3,  # References
    forward_depth=2,   # Citing papers
    max_papers=1000,
    include_citations=True
)

# Export network for Gephi
mapper.export_network(network, format="gexf", file="network.gexf")

2. Identify Seminal Works#

Use centrality metrics to find field-defining papers.

python

# Calculate centrality metrics
centrality = mapper.calculate_centrality(
    network=network,
    metrics=["betweenness", "eigenvector", "pagerank"]
)

# Identify seminal papers
seminal = mapper.identify_seminal_works(
    centrality=centrality,
    min_citations=100,
    top_n=20
)

for paper in seminal:
    print(f"{paper.title}: {paper.centrality_score}")

3. Discover Research Clusters#

Detect communities and emerging research topics.

python

# Detect research clusters
clusters = mapper.detect_clusters(
    network=network,
    algorithm="louvain",
    resolution=1.0
)

# Analyze cluster topics
for cluster_id, cluster in clusters.items():
    topic = mapper.extract_cluster_topic(cluster)
    print(f"Cluster {cluster_id}: {topic}")
    print(f"  Size: {cluster.size} papers")
    print(f"  Growth rate: {cluster.growth_rate}")

4. Generate Interactive Visualizations#

Create publication-ready network visualizations.

python

# Create interactive visualization
viz = mapper.visualize(
    network=network,
    layout="force_directed",
    node_color="publication_year",
    node_size="citation_count",
    edge_color="citation_type",
    interactive=True
)

# Save as HTML for web
viz.save_html("citation_network.html")

# Save static for publication
viz.save_pdf("figure_1.pdf", dpi=300)

Command Line Usage#

bash

python scripts/main.py --seed-pmid 12345678 --depth 2 --max-papers 500 --output network.json --visualize

Best Practices#

• Start with high-quality seed papers
• Set reasonable depth limits to avoid noise
• Validate key papers through multiple sources
• Update networks regularly as literature evolves

Quality Checklist#

Before using this skill, ensure you have:

• Clear understanding of your objectives
• Necessary input data prepared and validated
• Output requirements defined
• Reviewed relevant documentation

After using this skill, verify:

• Results meet your quality standards
• Outputs are properly formatted
• Any errors or warnings have been addressed
• Results are documented appropriately

References#

• references/guide.md - Comprehensive user guide
• references/examples/ - Working code examples
• references/api-docs/ - Complete API documentation

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Skill ID: 193 | Version: 1.0 | License: MIT