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Graph flattenability

Graph flattenability is a mathematics topic covered in the lgStudy science library. This page brings together a partial reference excerpt, illustrations, worked examples, real-world applications and a short study plan, so you can understand Graph flattenability rather than just read about it. In short: Flattenability in some d {\displaystyle d} -dimensional normed vector space is a property of graphs which states that any embedding, or drawing, of the graph in some high dimension d ′ {\displaystyle d'} can be "flattened" down to live in d {\displaystyle d} -dimensions, such that the distances between pairs of points connected by edges are preserved. A graph G {\displaystyle G} is d {\displaystyle d} -flattenable i…

Graph flattenability — main illustration
Graph flattenability — illustration

Key takeaways

  • Graph flattenability belongs to mathematics; place it in that map before memorising details.
  • Learn the definition first, then one example that makes the definition concrete.
  • Connect Graph flattenability to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Graph flattenability from memory before moving on to harder problems.

Reference excerpt

Flattenability in some d {\displaystyle d} -dimensional normed vector space is a property of graphs which states that any embedding, or drawing, of the graph in some high dimension d ′ {\displaystyle d'} can be "flattened" down to live in d {\displaystyle d} -dimensions, such that the distances between pairs of points connected by edges are preserved. A graph G {\displaystyle G} is d {\displaystyle d} -flattenable if every distance constraint system (DCS) with G {\displaystyle G} as its constraint graph has a d {\displaystyle d} -dimensional framework. Flattenability was first called realizability, but the name was changed to avoid confusion with a graph having some DCS with a d {\displaystyle d} -dimensional framework. Flattenability has connections to structural rigidity, tensegrities, Cayley configuration spaces, and a variant of the graph realization problem.

Definitions A distance constraint system ( G , δ ) {\displaystyle (G,\delta )} , where G = ( V , E ) {\displaystyle G=(V,E)} is a graph and δ : E → R | E | {\displaystyle \delta :E\rightarrow \mathbb {R} ^{|E|}} is an assignment of distances onto the edges of G {\displaystyle G} , is d {\displaystyle d} -flattenable in some normed vector space R d {\displaystyle \mathbb {R} ^{d}} if there exists a framework of ( G , δ ) {\displaystyle (G,\delta )} in d {\displaystyle d} -dimensions. A graph G = ( V , E ) {\displaystyle G=(V,E)} is d {\displaystyle d} -flattenable in R d {\displaystyle \mathbb {R} ^{d}} if every distance constraint system with G {\displaystyle G} as its constraint graph is d {\displaystyle d} -flattenable. Flattenability can also be defined in terms of Cayley configuration spaces; see connection to Cayley configuration spaces below.

Properties Closure under subgraphs. Flattenability is closed under taking subgraphs. To see this, observe that for some graph G {\displaystyle G} , all possible embeddings of a subgraph H {\displaystyle H} of G {\displaystyle G} are contained in the set of all embeddings of G {\displaystyle G} . Minor-closed. Flattenability is a minor-closed property by a similar argument as above. Flattening dimension. The flattening dimension of a flattenable graph G {\displaystyle G} in some normed vector space is the lowest dimension d {\displaystyle d} such that G {\displaystyle G} is d {\displaystyle d} -flattenable. The flattening dimension of a graph is closely related to its gram dimension. The following is an upper-bound on the flattening dimension of an arbitrary graph under the l 2 {\displaystyle l_{2}} -norm. Theorem. The flattening dimension of a graph G = ( V , E ) {\displaystyle G=\left(V,E\right)} under the l 2 {\displaystyle l_{2}} -norm is at most O ( | E | ) {\displaystyle O\left({\sqrt {\left|E\right|}}\right)} . For a detailed treatment of this topic, see Chapter 11.2 of Deza & Laurent.

Euclidean flattenability This section concerns flattenability results in Euclidean space, where distance is measured using the l 2 {\displaystyle l_{2}} norm, also called the Euclidean norm.

1-flattenable graphs The following theorem is folklore and shows that the only forbidden minor for 1-flattenability is the complete graph K 3 {\displaystyle K_{3}} . Theorem. A graph is 1-flattenable if and only if it is a forest.

… excerpt ends here. Continue reading the full article.

Illustrations

Graph flattenability: Figure 2. For certain linkages, this graph has a 1-dimensional realization (e.g. the assignment of 1 to every edge). However, 
  
    
      
        
          C
          
            3
          
        
      
    
    {\displaystyle C_{3}}
  
 can be obtained by contracting the edge 
  
    
      
        
          
            
              
                A
                B
              
              ¯
            
          
        
      
    
    {\displaystyle {\bar {AB}}}
  
, so the graph is not 1-flattenable.
Figure 2. For certain linkages, this graph has a 1-dimensional realization (e.g. the assignment of 1 to every edge). However, C 3 {\displaystyle C_{3}} can be obtained by contracting the edge A B ¯ {\displaystyle {\bar {AB}}} , so the graph is not 1-flattenable.
Graph flattenability: Figure 3. The graphs of interest for 3-flattenability.
Figure 3. The graphs of interest for 3-flattenability.
Graph flattenability: Figure 4. Construction steps to show the 1-skeleton of an octahedron is not 3-flattenable.[1]
Figure 4. Construction steps to show the 1-skeleton of an octahedron is not 3-flattenable.[1]

Worked examples

Example 1 — a first encounter with Graph flattenability

Start with the simplest possible case. Write down what Graph flattenability claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In mathematics, the smallest case is usually a single object, a single equation or a single measurement. Check that every symbol or term in your sentence has a meaning in that case.

Example 2 — changing one variable

Take the situation from Example 1 and change exactly one quantity: double it, halve it, or set it to zero. Predict what should happen to Graph flattenability before you calculate. Comparing your prediction with the result is the fastest way to find out whether you understand the idea or only the words.

Example 3 — an exam-style question

Typical questions about Graph flattenability ask you to (a) state it precisely, (b) apply it to given data, and (c) explain a limitation. Practise writing all three answers in under five minutes; the third part is what separates a full-mark answer from an average one.

Applications of Graph flattenability

In research
Graph flattenability appears in mathematics research whenever the underlying quantities have to be modelled precisely. Papers usually cite it as a starting assumption and then explore where it breaks down.
In technology and industry
Engineering practice reuses Graph flattenability in design rules, simulations and safety margins. Knowing the idea lets you read a specification sheet and understand why the numbers look the way they do.
In the classroom
Graph flattenability is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph theory, Mathematics of rigidity, so understanding it makes those chapters shorter.
In everyday life
Look for Graph flattenability outside the textbook — in sport, cooking, traffic, electronics or the sky above you. An example you found yourself is remembered far longer than one you were given.
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How to study Graph flattenability in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Graph flattenability means in your own words.
  3. Compare your version with the excerpt and mark what you missed.
  4. Work through the three examples above with pen and paper.
  5. Explain Graph flattenability out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Graph flattenability in simple terms?

Flattenability in some d {\displaystyle d} -dimensional normed vector space is a property of graphs which states that any embedding, or drawing, of the graph in some high dimension d ′ {\displaystyle d'} can be "flattened" down to live in d {\displaystyle d} -dimensions, such that the distances bet…

Why does Graph flattenability matter?

Because it connects several mathematics ideas at once: it gives you a definition you can apply, a quantity you can calculate, and a way to check whether a result is plausible.

How should I study Graph flattenability?

Read the excerpt, restate it from memory, then work through the examples and applications listed on this page. The five-step study plan above takes about twenty minutes.

What does this page cover?

It gives you a compact reference excerpt plus original lgStudy explanations, examples, applications and study material on Graph flattenability.

Tags

  • Graph theory
  • Mathematics of rigidity

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