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.

# 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.

# 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.

# 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

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

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

citation-chasing-mapping

Scientific Citation Network and Knowledge Mapper

When to Use This Skill

Quick Start