Use when identifying seminal papers in a research field, mapping research lineage and intellectual heritage, discovering related work through reference tracking, or finding potential collaborators through co-citation analysis. Maps citation networks to trace research evolution, identify influential papers, and discover hidden connections in scientific literature. Supports systematic reviews, bibliometric analysis, and research planning through comprehensive citation tracking.
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"
)
Construct bidirectional citation graphs from seed papers with configurable depth.
# 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")
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}")
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}")
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)
python scripts/main.py --seed-pmid 12345678 --depth 2 --max-papers 500 --output network.json --visualize
Before using this skill, ensure you have:
After using this skill, verify:
references/guide.md - Comprehensive user guidereferences/examples/ - Working code examplesreferences/api-docs/ - Complete API documentationSkill ID: 193 | Version: 1.0 | License: MIT