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

Metric	Level	Definition	Interpretation
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

Building Citation Networks

Data Sources

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

Fetching Data with OpenAlex

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]

Building the Network with NetworkX

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()}")

Network Analysis

Computing Centrality Metrics

# 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")

Co-Citation Analysis

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

Visualization

Using pyvis for Interactive Visualization

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}")

Using VOSviewer

VOSviewer is a dedicated bibliometric visualization tool:

1. Export records from Web of Science or Scopus in CSV format.
2. Open VOSviewer and select "Create a map based on bibliographic data."
3. Choose analysis type: co-citation, bibliographic coupling, or co-authorship.
4. Adjust parameters (minimum citations, cluster resolution).
5. Export high-resolution images for publications.

Altmetrics

Traditional citations take years to accumulate. Altmetrics capture immediate attention:

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

Best Practices

• Combine multiple data sources. No single database has complete coverage. Merge OpenAlex and CrossRef for best results.
• Normalize by field and age. A 2024 paper in biology and a 2024 paper in mathematics have very different citation rate baselines.
• Use relative indicators. Field-Weighted Citation Impact (FWCI) accounts for disciplinary differences.
• Do not equate citations with quality. Retracted papers sometimes have high citation counts. Controversial papers accumulate criticism citations.
• Report network metrics alongside standard citations. PageRank and betweenness reveal influence that raw counts miss.
• Update analyses periodically. Citation networks change as new papers are published.

References

• OpenAlex API -- Free, open bibliographic data
• CrossRef API -- DOI resolution and metadata
• VOSviewer -- Bibliometric visualization tool
• bibliometrix R package -- Comprehensive bibliometric analysis
• Altmetric -- Alternative impact metrics
• NetworkX Documentation -- Python graph analysis library