Citation Network GuideSAFE

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---
name: citation-network-guide
description: "Analyze citation networks, impact metrics, and bibliometric patterns"
metadata:
  openclaw:
    emoji: "🕸"
    category: "literature"
    subcategory: "metadata"
    keywords: ["citation statistics", "citation tracking", "citation analysis", "citation network", "altmetrics"]
    source: "N/A"
---

# 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

```python
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

```python
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

```python
# 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

```python
def build_cocitation_network(papers_with_refs):
    """Build co-citation network from papers with their reference lists."""
    from itertools import combinat

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