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Gray graph: Art, Construction & Overview
In the mathematical field of graph theory, the Gray graph is an undirected bipartite graph with 54 vertices and 81 edges. It is a cubic graph: every vertex touches exactly three edges. It was discovered by Marion C. Gray in 1932 (unpublished), then discovered independently by Bouwer 1968 in reply to a question posed by Jon Folkman 1967. The Gray graph is…
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Gray graph topic overview
The analysis highlights Art, Construction and Overview as prominent areas in the source structure around Gray graph.
Related topics
27
Source areas
4
Connected nodes
31
Extracted relationships
81
Concept neighborhoods
22
Bridge connections
31
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 · 14 topics
Construction · 9 topics
Algebraic properties · 3 topics
Geometric properties · 1 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.
Key facts & relationships
High-confidence facts extracted from structured source data. Use them as anchors for further research.
Automorphisms
1296
Book thickness
3
Chromatic index
3
Chromatic number
2
Diameter
6
Edges
81
Explore all related topics Closing gaps
A structured outline of related entities, concepts and subtopics. Open any item to build a new map centered on it.
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.
Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.
How Gray graph connects Entity context
Entity context groups extracted connections by relationship type. Counts show recurrence in the analyzed source; the examples are research signals, not claims about importance.
The extracted context around Gray graph shows recurring relationship patterns in the source. For example, Gray graph → AID-JGT1, An, Bouwer, Canadian Mathematical Bulletin, CMB-1968-063-0, CO, Combinatorial Theory, Combinatorics, December, Dragan, European Journal, Folkman, Graph Theory, Gray, Ivic-Weiss, Izak Bouwer, Izak Zurk Bouwer, Journal, Marušič, Monson Another extracted example is Gray graph → As, Bouwer, Gray, In, It, Ivic-Weiss, Levi, Marušič, Monson, Pisanski, Schulte, The, The Gray, This, Wilson. Use these groups to spot repeated connection types before inspecting the individual relationships.
Gray graph → As, Bouwer, Gray, In, It, Ivic-Weiss, Levi, Marušič, Monson, Pisanski, Schulte, The, The Gray, This, Wilson
related to External links· 6
Gray graph → Eric, Its Kind, MathWorld, Smallest Graph, The Gray Graph Is, Weisstein
is a· 5
Gray graph → group of order 1296, Levi graph of this configuration, semi-symmetric graph, Smallest Graph of Its Kind, undirected bipartite graph with 54 vertices and 81 edges
related to Algebraic properties· 4
Gray graph → Gray, It, The, Therefore
Automorphisms· 1
Gray graph → 1296
Book thickness· 1
Gray graph → 3
Chromatic index· 1
Gray graph → 3
Chromatic number· 1
Gray graph → 2
Diameter· 1
Gray graph → 6
Important terminology
Frequent words and multi-word phrases across the lead, headings, infobox and body. Useful for terminology coverage.
Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.
Relationships are represented as Subject → Predicate → Object triples. Confidence describes extraction strength, not the real-world importance of a relationship; lower-confidence rows may be contextual rather than explicit.
TTTA extracted 81 structured relationships around Gray graph. Examples in this analysis include Gray graph → Automorphisms → 1296 and Gray graph → Book thickness → 3. The table shows each extracted connection, where it came from and its confidence.
Subject
Predicate
Object
Confidence
Src
Gray graph
Automorphisms
1296
1.00
infobox
Gray graph
Book thickness
3
1.00
infobox
Gray graph
Chromatic index
3
1.00
infobox
Gray graph
Chromatic number
2
1.00
infobox
Gray graph
Diameter
6
1.00
infobox
Gray graph
Edges
81
1.00
infobox
Gray graph
Genus
7
1.00
infobox
Gray graph
Girth
8
1.00
infobox
Gray graph
Named after
Marion Cameron Gray
1.00
infobox
Gray graph
Properties
Cubic Semi-symmetric Hamiltonian Bipartite
1.00
infobox
Gray graph
Queue number
2
1.00
infobox
Gray graph
Radius
6
1.00
infobox
Gray graph
Vertices
54
1.00
infobox
Gray graph
is a
undirected bipartite graph with 54 vertices and 81 edges
0.90
text
Gray graph
is a
Levi graph of this configuration
0.90
text
Gray graph
is a
group of order 1296
0.90
text
Gray graph
is a
semi-symmetric graph
0.90
text
Gray graph
is a
Smallest Graph of Its Kind
0.90
text
No triples extracted yet.
Related concept clusters Concept neighborhoods
Concept neighborhoods group terms that sit near one another in the extracted graph. They are useful for discovering adjacent language and context, but proximity does not by itself imply a factual relationship.
The concept neighborhoods around Gray graph bring nearby vocabulary together. In this analysis, examples include Gray, Pisanski and Marušič. Use the clusters to find adjacent concepts and terminology that may deserve separate research.
Gray graph
Gray
Pisanski
Marušič
Cubic
Vertices
Algebraic
Chromatic
Genus
Index
Bipartite
Levi
Edges
gray graph
Gray
Pisanski
Cubic
Vertices
Edges
Marušič
Vertex
Bipartite
Levi
Edge
Doi
Algebraic
graph theory
Gray
Journal
Combinatorial
Doi
Graphs
Regular
Pisanski
Cubic
Vertices
Edges
Vertex
Bipartite
bipartite graph
Gray
Vertices
Lines
Edges
Points
Pisanski
Cubic
Graphs
Vertex
Diameter
Hamiltonian
Marion
vertices
Bipartite
Points
Lines
Edges
Graph
Graphs
Gray
Diameter
Hamiltonian
Marion
Mathematical
Number
edges
Every
Vertices
Vertex
Graph
Cubic
Diameter
Hamiltonian
Marion
Mathematical
Number
Properties
Radius
cubic graph
Gray
Constructed
Vertex
Hamiltonian
Pisanski
Algebraic
Chromatic
Cubic
Graph
Index
Three
Transitive
marion c. gray
Hamiltonian
Number
Properties
Radius
Pisanski
Constructed
Genus
Marušič
Lines
Cubic
Vertices
Points
Connections between topic areas Semantic bridges
Semantic bridges connect parts of the graph that would otherwise be more separate. A bridge is a structural signal in this analysis, not a claim that the two topic areas are equivalent.
For Gray graph, one of the stronger structural bridges in this analysis connects Gray graph with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.
Number of nodes, edges, triples, density and central hubs. Use it to gauge the size and connectivity of the map.
Gray graph
Nodes32
Edges31
Triples81
Avg. degree1.94
Density0.0625
Components1
Source & methodology
TTTA analyzes the structure around Gray graph to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Art, Construction & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.
