Citation Chasing Mapping

Source: https://github.com/aipoch/medical-research-skills

Scientific Citation Network and Knowledge Mapper

When to Use

• Use this skill when the task needs 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.
• Use this skill for evidence insight tasks that require explicit assumptions, bounded scope, and a reproducible output format.
• Use this skill when you need a documented fallback path for missing inputs, execution errors, or partial evidence.

Key Features

• Scope-focused workflow aligned to: 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.
• Packaged executable path(s): scripts/main.py.
• Reference material available in references/ for task-specific guidance.
• Structured execution path designed to keep outputs consistent and reviewable.

Dependencies

• Python: 3.10+. Repository baseline for current packaged skills.
• Third-party packages: not explicitly version-pinned in this skill package. Add pinned versions if this skill needs stricter environment control.

Example Usage

cd "20260318/scientific-skills/Evidence Insight/citation-chasing-mapping"
python -m py_compile scripts/main.py
python scripts/main.py --help

Example run plan:

1. Confirm the user input, output path, and any required config values.
2. Edit the in-file CONFIG block or documented parameters if the script uses fixed settings.
3. Run python scripts/main.py with the validated inputs.
4. Review the generated output and return the final artifact with any assumptions called out.

Implementation Details

See ## Workflow above for related details.

• Execution model: validate the request, choose the packaged workflow, and produce a bounded deliverable.
• Input controls: confirm the source files, scope limits, output format, and acceptance criteria before running any script.
• Primary implementation surface: scripts/main.py.
• Reference guidance: references/ contains supporting rules, prompts, or checklists.
• Parameters to clarify first: input path, output path, scope filters, thresholds, and any domain-specific constraints.
• Output discipline: keep results reproducible, identify assumptions explicitly, and avoid undocumented side effects.

Quick Check

Use this command to verify that the packaged script entry point can be parsed before deeper execution.

python -m py_compile scripts/main.py

Audit-Ready Commands

Use these concrete commands for validation. They are intentionally self-contained and avoid placeholder paths.

python -m py_compile scripts/main.py
python scripts/main.py --help

Workflow

1. Confirm the user objective, required inputs, and non-negotiable constraints before doing detailed work.
2. Validate that the request matches the documented scope and stop early if the task would require unsupported assumptions.
3. Use the packaged script path or the documented reasoning path with only the inputs that are actually available.
4. Return a structured result that separates assumptions, deliverables, risks, and unresolved items.
5. If execution fails or inputs are incomplete, switch to the fallback path and state exactly what blocked full completion.

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

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.

# 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")