43-wentorai-research-plugins-skills-literature-metadata-citation-network-guide

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SKILL.md

Citation Network Analysis Guide

Overview

Citation networks encode the intellectual structure of scientific fields. By analyzing who cites whom, how ideas propagate, and where research clusters form, researchers can identify foundational papers, emerging trends, influential authors, and gaps in the literature that represent opportunities for new contributions.

This guide covers the theory and practice of citation network analysis: building networks from bibliographic data, computing standard metrics (h-index, impact factor, PageRank, betweenness centrality), visualizing network structure, and interpreting results. It also covers altmetrics -- alternative impact measures that capture attention beyond traditional citations.

Whether you are conducting a systematic literature review, mapping a new research area, evaluating potential collaborators, or assessing the impact of your own work, citation network analysis provides quantitative tools to complement qualitative judgment.

Core Concepts

Types of Citation Networks

Network Type	Nodes	Edges	Question Answered
Direct citation	Papers	Paper A cites Paper B	Which papers directly build on each other?
Co-citation	Papers	A and B are both cited by C	Which papers are perceived as related?
Bibliographic coupling	Papers	A and B both cite C	Which papers share intellectual foundations?
Author co-citation	Authors	Two authors are frequently co-cited	Which researchers are seen as working in the same area?
Author collaboration	Authors	Two authors co-authored a paper	Who works together?

Key Metrics

Source	Coverage	API	Cost
OpenAlex	250M+ works, all disciplines	REST API, free	Free (no key required)
OpenAlex	250M+ works, all disciplines	REST API, free	Free
Crossref	140M+ DOIs	REST API	Free
Web of Science	Curated, multi-disciplinary	Institutional	Licensed
Scopus	90M+ records	REST API	Licensed

import requests
import networkx as nx

def get_citations(doi, max_results=100):
    """Fetch papers that cite a given DOI using OpenAlex."""
    work_url = f"https://api.openalex.org/works/doi:{doi}"
    resp = requests.get(work_url)
    work = resp.json()
    openalex_id = work['id']

    # Get citing works
    citing_url = (
        f"https://api.openalex.org/works"
        f"?filter=cites:{openalex_id}"
        f"&per_page={max_results}"
        f"&sort=cited_by_count:desc"
    )
    citing_resp = requests.get(citing_url)
    citing_works = citing_resp.json()['results']

    return [{
        'id': w['id'],
        'title': w['title'],
        'year': w['publication_year'],
        'cited_by_count': w['cited_by_count'],
        'authors': [a['author']['display_name']
                    for a in w.get('authorships', [])]
    } for w in citing_works]

import networkx as nx

def build_citation_network(seed_doi, depth=2):
    """Build a citation network to specified depth from a seed paper."""
    G = nx.DiGraph()
    visited = set()
    queue = [(seed_doi, 0)]

    while queue:
        doi, level = queue.pop(0)
        if doi in visited or level > depth:
            continue
        visited.add(doi)

        citations = get_citations(doi, max_results=20)
        for paper in citations:
            G.add_node(paper['id'],
                       title=paper['title'],
                       year=paper['year'],
                       citations=paper['cited_by_count'])
            G.add_edge(paper['id'], doi)  # citing -> cited

            if level + 1 <= depth:
                queue.append((paper['id'], level + 1))

    return G

# Build and analyze
G = build_citation_network("10.1234/example", depth=2)
print(f"Nodes: {G.number_of_nodes()}, Edges: {G.number_of_edges()}")

# PageRank (most important papers in the network)
pagerank = nx.pagerank(G, alpha=0.85)
top_papers = sorted(pagerank.items(), key=lambda x: x[1], reverse=True)[:10]

for paper_id, score in top_papers:
    title = G.nodes[paper_id].get('title', 'Unknown')
    print(f"  PR={score:.4f}: {title[:80]}")

# Betweenness centrality (bridge papers)
betweenness = nx.betweenness_centrality(G)
bridges = sorted(betweenness.items(), key=lambda x: x[1], reverse=True)[:10]

# Community detection (research clusters)
from networkx.algorithms.community import greedy_modularity_communities
communities = list(greedy_modularity_communities(G.to_undirected()))
print(f"Detected {len(communities)} research clusters")

def build_cocitation_network(papers_with_refs):
    """Build co-citation network from papers with their reference lists."""
    from itertools import combinations
    G = nx.Graph()

    for paper in papers_with_refs:
        refs = paper['references']
        for a, b in combinations(refs, 2):
            if G.has_edge(a, b):
                G[a][b]['weight'] += 1
            else:
                G.add_edge(a, b, weight=1)

    return G

from pyvis.network import Network

def visualize_citation_network(G, output='citation_network.html'):
    net = Network(height='800px', width='100%', directed=True)

    for node in G.nodes():
        title = G.nodes[node].get('title', str(node))[:60]
        size = min(5 + G.nodes[node].get('citations', 0) * 0.1, 40)
        net.add_node(str(node), label=title, size=size,
                     title=G.nodes[node].get('title', ''))

    for u, v in G.edges():
        net.add_edge(str(u), str(v))

    net.set_options("""
    var options = {
      "physics": {"barnesHut": {"gravitationalConstant": -3000}},
      "nodes": {"font": {"size": 10}}
    }
    """)
    net.save_graph(output)
    print(f"Saved to {output}")

Metric	Source	Speed	What It Measures
Altmetric Attention Score	altmetric.com	Hours	Media, social, policy mentions
PlumX metrics	Elsevier	Days	Usage, captures, mentions, social
Downloads	Publisher	Immediate	Reader interest
Twitter/X mentions	Social media	Immediate	Public discourse
Wikipedia citations	Wikipedia	Weeks	Encyclopedic significance
Policy document citations	Overton	Months	Policy relevance

Citation count	Paper	Number of times cited	Raw impact
h-index	Author	h papers with >= h citations	Sustained productivity
Impact Factor	Journal	Mean citations to recent articles	Journal prestige
PageRank	Paper/Author	Iterative importance based on network position	Influence (weighted by citing paper importance)
Betweenness centrality	Paper	Frequency on shortest paths between other nodes	Bridging role between subfields
Burst detection	Paper/Term	Sudden increase in citations/usage	Emerging topic

43-wentorai-research-plugins-skills-literature-metadata-citation-network-guide

Summary

SKILL.md

Citation Network Analysis Guide

Overview

Core Concepts

Types of Citation Networks

Key Metrics

Building Citation Networks

Data Sources

Fetching Data with OpenAlex

Building the Network with NetworkX

Network Analysis

Computing Centrality Metrics

Co-Citation Analysis

Visualization

Using pyvis for Interactive Visualization

Using VOSviewer

Altmetrics

Best Practices

References

Related skills