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Eigenfactor: Science & Overview

The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked…

Language: English [EN]
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Eigenfactor topic overview

The analysis highlights Science and Overview as prominent areas in the source structure around Eigenfactor.

Related topics
8
Source areas
1
Connected nodes
9
Concept neighborhoods
9
Bridge connections
9

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Overview · 8 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How Eigenfactor connects Entity context

See recurring relationship patterns around Eigenfactor before inspecting the individual extracted relationships.

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

score journal impact importance journals citations scores total number incoming citation factor scientific larger measure higher metrics article influence considering

Eigenfactor relationships Subject–Predicate–Object triples

TTTA extracted structured relationships around Eigenfactor. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc

Related concept clusters Concept neighborhoods

The concept neighborhoods around Eigenfactor bring nearby vocabulary together. In this analysis, examples include Score, Citations and Importance. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Eigenfactor
    • Score
    • Citations
    • Importance
    • Journals
    • Scores
    • Journal
    • Incoming
    • Number
    • Total
    • Impact
    • Considering
    • Higher
  • eigenfactor
    • Score
    • Citations
    • Importance
    • Journals
    • Scores
    • Journal
    • Incoming
    • Number
    • Total
    • Impact
    • Considering
    • Higher
  • scientific journal
    • Score
    • University
    • Washington
    • West
    • Impact
    • Articles
    • Average
    • Considering
    • Journal
    • Measure
    • Scientific
    • Thought
  • impact factor
    • Factor
    • Impact
    • Journal
    • Also
    • Influence
    • Measures
    • Pagerank
    • Thought
    • Incoming
    • Score
    • Article
    • Articles
  • carl bergstrom
    • Bergstrom
    • Carl
    • Developed
    • Jevin
    • Rating
    • University
    • Washington
    • West
    • Scientific
    • Total
    • Importance
    • Journal
  • pagerank
    • Also
    • Citation
    • Factor
    • Impact
    • Journal
    • Eigenfactor
  • author-level
    • Measures
    • Importance
    • Scores
    • Eigenfactor
  • university of washington
    • Washington
    • West
    • Total

Connections between topic areas Semantic bridges

Bridges highlight paths between different parts of the Eigenfactor map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

Map overview Semantic statistics

Eigenfactor

Nodes10
Edges9
Triples0
Avg. degree1.8
Density0.2
Components1

Source & methodology

TTTA analyzes the structure around Eigenfactor to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Science & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — Eigenfactor · EN edition · Analysis: TopicsToTalkAbout

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