--- name: citation-network-builder-2148aeaf425a description: "Build and analyze citation networks from academic reference data" metadata: openclaw: emoji: "🕸️" category: "tools" subcategory: "knowledge-graph" keywords: ["citation network", "bibliometrics", "graph analysis", "co-citation", "bibliographic coupling", "network visualization"] source: "wentor-research-plugins" --- # Citation Network Builder A skill for constructing, analyzing, and visualizing citation networks from academic reference data. Covers data collection from bibliographic databases, network construction using direct citation, co-citation, and bibliographic coupling methods, community detection for identifying research clusters, and practical visualization with tools like Gephi, VOSviewer, and Python NetworkX. ## Data Collection and Preparation ### Source Databases Citation network analysis requires structured bibliographic data with reference lists. The choice of database determines coverage and available metadata. ``` Database Comparison for Citation Analysis: Web of Science (Clarivate): - Format: ISI/WoS plain text, BibTeX, CSV - Coverage: ~21,000 journals, back to 1900 - Strengths: Cited reference data is most complete - Limits: 1,000 records per export, subscription required - Best for: High-quality citation network analysis Scopus (Elsevier): - Format: CSV, BibTeX, RIS - Coverage: ~27,000 journals, back to 1970s for most - Strengths: Broader coverage than WoS, author IDs - Limits: 2,000 records per export, subscription required - Best for: Broader disciplinary coverage OpenAlex (free): - Format: JSON via REST API - Coverage: ~250M works, all disciplines - Strengths: Free, open, comprehensive, API access - Limits: Reference linking less complete than WoS - Best for: Large-scale analysis, reproducible research CrossRef (free): - Format: JSON via REST API - Coverage: ~150M DOIs across all publishers - Strengths: Free, authoritative DOI metadata, reference linking - Limits: No abstract text, citation counts may lag - Best for: Cross-publisher networks, DOI resolution ``` ### Data Cleaning for Network Construction ```python import pandas as pd def clean_bibliographic_data(records): """ Clean and deduplicate bibliographic records for network construction. Steps: 1. Standardize DOIs (lowercase, strip prefixes) 2. Deduplicate by DOI, then by title similarity 3. Parse reference lists into structured format 4. Filter records missing key fields """ # Standardize DOIs records["doi"] = ( records["doi"] .str.lower() .str.replace("https://doi.org/", "", regex=False) .str.replace("http://dx.doi.org/", "", regex=False) .str.strip() ) # Remove duplicates by DOI records = records.drop_duplicates(subset="doi", keep="first") # Filter records without references (cannot build citation links) records = records[records["references"].notna()] records = records[records["references"].str.len() > 0] return records ``` ## Network Construction Methods ### Direct Citation Network The simplest approach: paper A cites paper B creates a directed edge from A to B. ```python import networkx as nx def build_direct_citation_network(records): """ Build a directed citation network. Nodes = papers, Edges = citation relationships. Args: records: DataFrame with 'doi' and 'references' columns where 'references' is a list of cited DOIs Returns: NetworkX DiGraph """ G = nx.DiGraph() for _, row in records.iterrows(): citing_doi = row["doi"] G.add_node(citing_doi, title=row.get("title", ""), year=row.get("year", None)) for ref_doi in row["references"]: G.add_edge(citing_doi, ref_doi) return G ``` ### Co-Citation Network Two papers are co-cited when a third paper cites both. Co-citation strength is the number of papers that cite both. This method identifies intellectual relationships between cited works. ```python from itertools import combinations from collections import Counter def build_cocitation_network(records, min_cocitations=2): """ Build an undirected co-citation network. Nodes = cited papers, Edges = co-citation frequency. """ pair_counts = Counter() for _, row in records.iterrows(): refs = sorted(set(row["references"])) for a, b in combinations(refs, 2): pair_counts[(a, b)] += 1 G = nx.Graph() for (a, b), count in pair_counts.items(): if count >= min_cocitations: G.add_edge(a, b, weight=count) return G ``` ### Bibliographic Coupling Network Two papers are bibliographically coupled when they share one or more references. This method groups papers with similar theoretical or methodological foundations. ```python def build_bibliographic_coupling_network(records, min_shared=3): """ Build an undirected bibliographic coupling network. Nodes = citing papers, Edges = number of shared references. """ ref_sets = {} for _, row in records.iterrows(): ref_sets[row["doi"]] = set(row["references"]) G = nx.Graph() dois = list(ref_sets.keys()) for i in range(len(dois)): for j in range(i + 1, len(dois)): shared = len(ref_sets[dois[i]] & ref_sets[dois[j]]) if shared >= min_shared: G.add_edge(dois[i], dois[j], weight=shared) return G ``` ## Network Analysis ### Key Metrics ``` Node-level metrics: - In-degree (direct citation): number of times a paper is cited -> identifies influential papers - Betweenness centrality: how often a node lies on shortest paths -> identifies bridging papers connecting subfields - PageRank: iterative importance score based on who cites the paper -> identifies papers cited by other influential papers Network-level metrics: - Density: proportion of possible edges that exist - Clustering coefficient: tendency of nodes to form triangles - Average path length: mean shortest path between node pairs - Number of connected components: isolated clusters ``` ### Community Detection Community detection algorithms identify clusters of densely connected papers, corresponding to research subfields or intellectual traditions. ```python import community as community_louvain def detect_communities(G): """ Detect communities using the Louvain algorithm. Returns a dictionary mapping node -> community_id. """ partition = community_louvain.best_partition(G, weight="weight") # Summarize communities communities = {} for node, comm_id in partition.items(): communities.setdefault(comm_id, []).append(node) for comm_id, members in sorted(communities.items()): print(f"Community {comm_id}: {len(members)} papers") return partition ``` ## Visualization ### Tool Recommendations ``` Gephi (desktop application): - Best for: Interactive exploration of medium networks (1k-50k nodes) - Layout algorithms: ForceAtlas2, Fruchterman-Reingold - Export: SVG, PDF, PNG - Workflow: Import GEXF/GraphML -> layout -> partition by community -> adjust sizes by centrality -> export VOSviewer (desktop application): - Best for: Bibliometric networks specifically - Direct import from WoS/Scopus export files - Built-in clustering and overlay visualizations - Limitation: less customizable than Gephi Python (matplotlib, pyvis): - Best for: Reproducible, scriptable visualizations - Use pyvis for interactive HTML network graphs - Use matplotlib for static publication-quality figures ``` Citation network analysis provides a quantitative lens on the structure of scientific knowledge, revealing invisible colleges, emerging research fronts, and foundational works that shape entire disciplines. --- Source attribution: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/tools/knowledge-graph/citation-network-builder/SKILL.md License: MIT License information: https://opensource.org/license/mit Original upstream instructions; downloaded from Mega AI Hub. ## Upstream license notice MIT License Copyright (c) 2026 Wentor AI Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. # Mega AI Hub resource metadata Creator / publisher: wentorai Original project: https://github.com/wentorai/research-plugins/blob/bf44b3cd617fa94c8a1b254c5d1987142ca3d631/skills/tools/knowledge-graph/citation-network-builder/SKILL.md Attribution basis: Source repository record Fields of work: Data & Analytics, Education & Research Roles: Data Scientist, Market Research Analyst, Researcher Specialties: API, Database Tools: Gephi, Python Networkx, Vosviewer Country applicability: Country not specified Classification basis: Source catalog labels Instruction SHA-256: 293d283b6ed4b043096f41612b051ae435a25ade77c416e0d8422bd021f794e8