citation-analysis

# Citation Analysis Analyze citation networks, compute bibliometric indicators, and identify research fronts using Semantic Scholar, OpenAlex, and CrossRef data. ## When to Use - "What's the h-index of this author?" - "Show me the citation network for this paper" - "Identify the most influential papers in this field" - "Map the co-authorship network in this area" - "What are the emerging research fronts in NLP?" - "Analyze citation trends for CRISPR papers over time" ## When NOT to Use - Finding papers by topic (use literature-search) - Reading or summarizing papers (use scienceclaw-summarization) - Writing papers (use paper-writing) - Statistical analysis unrelated to citations (use statsmodels-stats) ## Bibliometric Indicators ### Author-Level Metrics ```python import numpy as np def h_index(citations: list[int]) -> int: """Compute h-index from a list of citation counts.""" sorted_c = sorted(citations, reverse=True) h = 0 for i, c in enumerate(sorted_c): if c >= i + 1: h = i + 1 else: break return h def g_index(citations: list[int]) -> int: """Compute g-index: largest g such that top g papers have >= g^2 citations.""" sorted_c = sorted(citations, reverse=True) cumsum = np.cumsum(sorted_c) g = 0 for i in range(len(sorted_c)): if cumsum[i] >= (i + 1) ** 2: g = i + 1 return g def i10_index(citations: list[int]) -> int: """Number of papers with 10+ citations.""" return sum(1 for c in citations if c >= 10) ``` ### Paper-Level Metrics - **Citation count**: Raw count from Semantic Scholar / OpenAlex - **Field-weighted citation impact (FWCI)**: Citations / expected citations in field - **Percentile rank**: Position relative to same-year, same-field papers - **Citation velocity**: Citations per year since publication ### Journal-Level Metrics - **Impact Factor**: Citations in year N to papers published in N-1 and N-2 - **CiteScore**: Citations over 4 years / documents over 4 years - **h5-index**: h-index of articles published in the last 5 years ## Citation Network Analysis ### Build Citation Graph ```python import networkx as nx def build_citation_graph(papers: list[dict]) -> nx.DiGraph: """ Build a directed citation graph. Each paper dict should have 'paperId', 'title', 'citations', 'references'. Edge direction: citing -> cited. """ G = nx.DiGraph() for p in papers: G.add_node(p['paperId'], title=p['title'], year=p.get('year'), citations=p.get('citationCount', 0)) for ref in p.get('references', []): if ref.get('paperId'): G.add_edge(p['paperId'], ref['paperId']) for cit in p.get('citations', []): if cit.get('paperId'): G.add_edge(cit['paperId'], p['paperId']) return G ``` ### Key Network Metrics ```python def analyze_citation_network(G: nx.DiGraph) -> dict: """Compute key citation network metrics.""" results = {} results['num_papers'] = G.number_of_nodes() results['num_citations'] = G.number_of_edges() results['density'] = nx.density(G) # Most cited (highest in-degree) in_deg = dict(G.in_degree()) results['most_cited'] = sorted(in_deg.items(), key=lambda x: -x[1])[:10] # PageRank (identifies influential papers beyond raw citations) pr = nx.pagerank(G) results['pagerank_top'] = sorted(pr.items(), key=lambda x: -x[1])[:10] # Betweenness centrality (bridge papers connecting subfields) bc = nx.betweenness_centrality(G) results['bridge_papers'] = sorted(bc.items(), key=lambda x: -x[1])[:10] return results ``` ### Co-Authorship Network ```python def build_coauthor_graph(papers: list[dict]) -> nx.Graph: """Build undirected co-authorship graph.""" G = nx.Graph() for p in papers: authors = [a['name'] for a in p.get('authors', []) if a.get('name')] for i, a1 in enumerate(authors): G.add_node(a1) for a2 in authors[i+1:]: if G.has_edge(a1, a2): G[a1][a2]['weight'] += 1 else: G.add_edge(a1, a2, weight=1) return G def find_communities(G: nx.Graph) -> list: """Detect research communities via Louvain.""" from networkx.algorithms.community import louvain_communities return louvain_communities(G, resolution=1.0) ``` ## Research Front Detection ### Method: Co-Citation Clustering 1. Identify highly co-cited paper pairs (cited together frequently) 2. Cluster co-cited papers into research fronts 3. Label fronts by common keywords in citing papers ### Method: Citation Burst Detection 1. Track citation counts per year for a set of papers 2. Identify papers with sudden citation increases (Kleinberg burst detection) 3. Papers with recent bursts indicate active research fronts ### Method: Bibliographic Coupling 1. Two papers are coupled if they share references 2. Stronger coupling = more shared references 3. Cluster coupled papers to find parallel research streams ## Visualization ### Citation Trend Plot ```python import matplotlib.pyplot as plt def plot_citation_trend(papers: list[dict], output_path: str): """Plot citation counts over publication years.""" years = [p['year'] for p in papers if p.get('year')] fig, ax = plt.subplots(figsize=(8, 4)) ax.hist(years, bins=range(min(years), max(years)+2), edgecolor='black') ax.set_xlabel('Publication Year') ax.set_ylabel('Number of Papers') ax.set_title('Publication Trend') fig.tight_layout() fig.savefig(output_path, dpi=300) print(f"Saved: {output_path}") ``` ## Data Sources | Source | Endpoint | Free? | Rate Limit | |--------|----------|-------|------------| | Semantic Scholar | `/paper/{id}/citations`, `/paper/{id}/references` | Yes | 100/5min (no key) | | OpenAlex | `/works?cited_by={id}`, `/works?cites={id}` | Yes | 100k/day | | CrossRef | `/works/{doi}` (reference list) | Yes | Polite pool | ## Best Practices 1. Always use Semantic Scholar paperId or DOI as canonical identifiers 2. Normalize author names (handle variants: "J. Smith" vs "John Smith") 3. Filter self-citations when computing impact metrics 4. Use field-normalized metrics for cross-discipline comparisons 5. Report the date of data collection (citation counts change daily) 6. Visualize networks with node size proportional to citations ## Zero-Hallucination Rule - NEVER fabricate citation counts, h-indices, or paper metadata - All bibliometric data must come from tool results in the current session - If an API returns no data for an author/paper, report the empty result explicitly