Research any topic before you write.

Find related topics. | Discover entities. | See connections. | Build a topical map.

Book embedding: History & Applications

In graph theory, a book embedding is a generalization of planar embedding of a graph to embeddings in a book, a collection of half-planes all having the same line as their boundary. Usually, the vertices of the graph are required to lie on this boundary line, called the spine, and the edges are required to stay within a single half-plane. The book…

Language: English [EN]
Use the mouse wheel or two fingers (on touchscreens) to zoom in and out of the map.
100%
More settings
100% 100% 100% 100% 100%

Book embedding topic overview

The analysis highlights History and Applications as prominent areas in the source structure around Book embedding.

Related topics
120
Source areas
6
Connected nodes
126
Extracted relationships
69
Related term clusters
46
Bridge connections
126

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.

Properties · 43 topics
Applications · 30 topics
Overview · 22 topics
Definitions · 13 topics
Specific graphs · 9 topics
History · 3 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.

Start with your topic. Discover where to go next.

Explore different angles and find fresh ideas to shape your next piece of content.

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

History

Definitions

Specific graphs

Properties

Applications

For the semantics nerds

You can skip this section if you’re here for content ideas and keyword inspiration.

Advanced semantic analysis

How Book embedding connects Entity context

The extracted context around Book embedding shows recurring relationship patterns in the source. For example, Book embedding → Finding, Given, Hamiltonian, NP-complete, NP-hard, One, Since, Therefore, Unger Another extracted example is Book embedding → Book, Chung, Communication, CPUs, DIOGENES, Leighton, One, Rosenberg, VLSI. Use these groups to spot repeated connection types before inspecting the individual relationships.

Book embedding

Top relations

related to Computational complexity · 9
Book embedding → Finding, Given, Hamiltonian, NP-complete, NP-hard, One, Since, Therefore, Unger
related to Fault-tolerant multiprocessing · 9
Book embedding → Book, Chung, Communication, CPUs, DIOGENES, Leighton, One, Rosenberg, VLSI
related to history · 8
Book embedding → Atneosen, Gail Atneosen, Important, Kainen, Mihalis Yannakakis, Paul, Persinger, Taylor Ollmann
related to Stack sorting · 7
Book embedding → Another, As Chung, Chung, DonaldKnuth, Leighton, Rosenberg, Since
related to Planarity and outerplanarity · 6
Book embedding → Conversely, Every, Hamiltonian, Harary, The Goldner, Therefore
related to Computational complexity theory · 5
Book embedding → Pavan, Tewari, Thus, Turing, Vinodchandran
related to RNA folding · 5
Book embedding → Advantages, Alternatively, Haslinger, RNA, Stadler
related to Relation to other graph invariants · 4
Book embedding → Analogously, Book, Graphs, K2
related to Traffic control · 4
Book embedding → As Kainen, Thus, U-turn, U-turns
related to Graph drawing · 3
Book embedding → Book, Planar, Two

Important terminology

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

Important terminology

book graph thickness embedding graphs edges vertices two spine number embeddings planar also drawing page one given pages every line

Book embedding relationships Subject–Predicate–Object triples

TTTA extracted 69 structured relationships around Book embedding. Examples in this analysis include Book embedding → is a → generalization of planar embedding of a graph to embeddings in a book and Book embedding → is a → special case of a planar embedding. The table shows each extracted connection, where it came from and its confidence.

SubjectPredicateObjectConfidenceSrc
Book embeddingis ageneralization of planar embedding of a graph to embeddings in a book0.90text
Book embeddingis aspecial case of a planar embedding0.90text
this oneinstance ofDespite the existence of examples0.80text
Blankenshipinstance ofDespite the existence of examples0.80text
Book embeddingrelated to Computational complexityFinding0.60section
Book embeddingrelated to Computational complexityNP-hard0.60section
Book embeddingrelated to Computational complexityHamiltonian0.60section
Book embeddingrelated to Computational complexityNP-complete0.60section
Book embeddingrelated to Computational complexityTherefore0.60section
Book embeddingrelated to Computational complexityUnger0.60section
Book embeddingrelated to Computational complexityGiven0.60section
Book embeddingrelated to Computational complexityOne0.60section

Related concept clusters Related term clusters

The concept neighborhoods around Book embedding bring nearby vocabulary together. In this analysis, examples include Thickness, Graph and Embedding. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

  • Book embedding
    • Thickness
    • Graph
    • Embedding
    • Graphs
    • Two
    • Embeddings
    • Edges
    • Planar
    • Also
    • Spine
    • Vertices
    • Every
  • book embedding
    • Thickness
    • Graph
    • Embedding
    • Graphs
    • Spine
    • Two
    • Pages
    • Vertices
    • Embeddings
    • Edges
    • Planar
    • Also
  • graph theory
    • Thickness
    • Vertices
    • Edges
    • Two
    • Spine
    • Every
    • Given
    • Number
    • Planar
    • Edge
    • Also
    • Order
  • graph
    • Thickness
    • Vertices
    • Edges
    • Two
    • Spine
    • Every
    • Given
    • Number
    • Planar
    • Edge
    • Also
    • Order
  • line
    • Along
    • Drawn
    • Two-page
    • Drawing
    • Edges
    • Rna
    • Spine
    • May
    • Structure
    • Way
    • Single
    • Vertices
  • graph invariants
    • Thickness
    • Vertices
    • Edges
    • Two
    • Spine
    • Every
    • Given
    • Number
    • Planar
    • Edge
    • Also
    • Order
  • graph drawing
    • Thickness
    • Vertices
    • Edges
    • Two
    • Spine
    • Every
    • Given
    • Number
    • Planar
    • Two-page
    • Edge
    • Fixed
  • graph embedding
    • Thickness
    • Vertices
    • Graph
    • Edges
    • Two
    • Spine
    • Pages
    • Every
    • Given
    • Number
    • Planar
    • Along

Connections between topic areas Semantic bridges

For Book embedding, one of the stronger structural bridges in this analysis connects Book embedding with Properties. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3
Book embedding — Properties · splits 83 ⟂ 44
Book embedding — Applications · splits 96 ⟂ 31
Book embedding — Overview · splits 104 ⟂ 23
Book embedding — Definitions · splits 113 ⟂ 14
Book embedding — Specific graphs · splits 117 ⟂ 10
Book embedding — History · splits 123 ⟂ 4

Map overview Semantic statistics

Book embedding

Nodes127
Edges126
Triples69
Avg. degree1.98
Density0.015748
Components1

Source & methodology

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

Source: Wikipedia — Book embedding · EN edition · Analysis: TopicsToTalkAbout

For writers, content strategists, SEOs, marketers and creators — from quick topic research to advanced semantic analysis.

Monitor your Domain Rating with FrogDR