Skill: aipoch-ai/citation-chasing-mapping (version 0.1.0)

--- name: citation-chasing-mapping description: 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. allowed-tools: "Read Write Bash Edit" license: MIT metadata: skill-author: AIPOCH version: "1.0" --- # 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 --- **Skill ID**: 193 | **Version**: 1.0 | **License**: MIT


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