43-wentorai-research-plugins-skills-literature-discovery-literature-mapping-guide

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SKILL.md

Literature Mapping Guide

Build visual maps of scholarly literature to understand research landscapes, identify clusters of related work, and discover hidden connections between papers.

What Is Literature Mapping?

Literature mapping transforms flat lists of papers into interactive visual networks where nodes represent papers and edges represent citation or similarity relationships. This approach helps researchers:

See the overall structure of a research field at a glance
Identify seminal papers (highly connected nodes)
Discover clusters and subfields
Find bridging papers that connect disparate areas
Spot gaps where no papers exist

Tools for Visual Literature Mapping

Connected Papers

Connected Papers (connectedpapers.com) builds a similarity graph around a seed paper using co-citation and bibliographic coupling analysis.

Feature	Details
Input	Paper title, DOI, or URL
Graph type	Similarity (not direct citation)
Node size	Citation count
Node color	Publication year (darker = older)
Max nodes	~40 per graph
Cost	Free: 5 graphs/month; Premium: unlimited

How to use:

Enter a seed paper URL or title
The tool builds a graph of the most similar papers (regardless of direct citation links)
Click any node to see its abstract and bibliographic details
Use "Prior works" to find foundational papers and "Derivative works" for recent developments
Export the graph or paper list as BibTeX

Litmaps

Litmaps (litmaps.com) creates dynamic, multi-seed citation maps that update as new papers are published.

# VOSviewer supports several network types:
# - Co-authorship networks
# - Co-citation networks
# - Bibliographic coupling networks
# - Co-occurrence of keywords
# - Citation networks

# Input formats:
# - Web of Science export files
# - Scopus CSV exports
# - Dimensions export files
# - RIS files from reference managers
# - CrossRef API queries (built-in)

import networkx as nx
import requests
from collections import defaultdict

def build_citation_graph(seed_ids, depth=1, max_per_level=20):
    """Build a directed citation graph from seed papers."""
    G = nx.DiGraph()
    visited = set()
    queue = [(sid, 0) for sid in seed_ids]

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

        # Get paper metadata
        meta_resp = requests.get(
            f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}",
            params={"fields": "title,year,citationCount"}
        )
        if meta_resp.status_code != 200:
            continue
        meta = meta_resp.json()
        G.add_node(paper_id, title=meta.get("title", ""),
                   year=meta.get("year"), citations=meta.get("citationCount", 0))

        # Get references (backward)
        refs_resp = requests.get(
            f"https://api.semanticscholar.org/graph/v1/paper/{paper_id}/references",
            params={"fields": "title,year,citationCount", "limit": max_per_level}
        )
        if refs_resp.status_code == 200:
            for ref in refs_resp.json().get("data", []):
                cited = ref["citedPaper"]
                if cited.get("paperId"):
                    G.add_node(cited["paperId"], title=cited.get("title", ""),
                               year=cited.get("year"), citations=cited.get("citationCount", 0))
                    G.add_edge(paper_id, cited["paperId"], relation="cites")
                    if level < depth:
                        queue.append((cited["paperId"], level + 1))

    return G

# Build graph from 2 seed papers
seeds = ["DOI:10.1038/s41586-021-03819-2", "ARXIV:2005.14165"]
graph = build_citation_graph(seeds, depth=1, max_per_level=15)
print(f"Graph: {graph.number_of_nodes()} nodes, {graph.number_of_edges()} edges")

# Find most central papers
centrality = nx.betweenness_centrality(graph)
top_central = sorted(centrality.items(), key=lambda x: x[1], reverse=True)[:10]
for node_id, score in top_central:
    title = graph.nodes[node_id].get("title", "Unknown")
    print(f"  Centrality={score:.3f}: {title}")

Visual Feature	Interpretation
Large cluster	Established subfield with many related papers
Small isolated cluster	Emerging or niche research area
Bridge node between clusters	Interdisciplinary or foundational paper
Dense interconnections	Mature area with extensive cross-referencing
Sparse area between clusters	Potential research gap or opportunity
Temporal gradient (old to new)	Evolution of ideas over time

43-wentorai-research-plugins-skills-literature-discovery-literature-mapping-guide

Summary

SKILL.md

Literature Mapping Guide

What Is Literature Mapping?

Tools for Visual Literature Mapping

Connected Papers

Litmaps

VOSviewer

CiteSpace

Building a Custom Literature Map with Python

Interpreting Literature Maps

Best Practices

Related skills