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Linear arboricity

Linear arboricity is a science 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 Linear arboricity rather than just read about it. In short: In graph theory, a branch of mathematics, the linear arboricity of an undirected graph is the smallest number of linear forests its edges can be partitioned into. Here, a linear forest is an acyclic graph with maximum degree two; that is, it is a disjoint union of path graphs.

Linear arboricity — main illustration
Linear arboricity — illustration

Key takeaways

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

Reference excerpt

In graph theory, a branch of mathematics, the linear arboricity of an undirected graph is the smallest number of linear forests its edges can be partitioned into. Here, a linear forest is an acyclic graph with maximum degree two; that is, it is a disjoint union of path graphs. Linear arboricity is a variant of arboricity, the minimum number of forests into which the edges can be partitioned. The linear arboricity of any graph of maximum degree Δ {\displaystyle \Delta } is known to be at least ⌈ Δ / 2 ⌉ {\displaystyle \lceil \Delta /2\rceil } and is conjectured to be at most ⌈ ( Δ + 1 ) / 2 ⌉ {\displaystyle \lceil (\Delta +1)/2\rceil } . This conjecture would determine the linear arboricity exactly for graphs of odd degree, as in that case both expressions are equal. For graphs of even degree it would imply that the linear arboricity must be one of only two possible values, but determining the exact value among these two choices is NP-complete.

Relation to degree

The linear arboricity of a graph G {\displaystyle G} with maximum degree Δ {\displaystyle \Delta } is always at least ⌈ Δ / 2 ⌉ {\displaystyle \lceil \Delta /2\rceil } , because each linear forest can use only two of the edges at a maximum-degree vertex. The linear arboricity conjecture of Akiyama, Exoo & Harary (1981) is that this lower bound is also tight: according to their conjecture, every graph has linear arboricity at most ⌈ ( Δ + 1 ) / 2 ⌉ {\displaystyle \lceil (\Delta +1)/2\rceil } . However, this remains unproven, with the best proven upper bound on the linear arboricity being somewhat larger,

Δ / 2 + O ( Δ 2 / 3 − c ) {\displaystyle \Delta /2+O(\Delta ^{2/3-c})} for some constant c > 0 {\displaystyle c>0} due to Ferber, Fox and Jain. In order for the linear arboricity of a graph to equal Δ / 2 {\displaystyle \Delta /2} , Δ {\displaystyle \Delta } must be even and each linear forest must have two edges incident to each vertex of degree Δ {\displaystyle \Delta } . But at a vertex that is at the end of a path, the forest containing that path has only one incident edge, so the degree at that vertex cannot equal Δ {\displaystyle \Delta } . Thus, a graph whose linear arboricity equals Δ / 2 {\displaystyle \Delta /2} must have some vertices whose degree is less than maximum. In a regular graph, there are no such vertices, and the linear arboricity cannot equal Δ / 2 {\displaystyle \Delta /2} . Therefore, for regular graphs, the linear arboricity conjecture implies that the linear arboricity is exactly ⌈ ( Δ + 1 ) / 2 ⌉ {\displaystyle \lceil (\Delta +1)/2\rceil } .

Related problems Linear arboricity is a variation of arboricity, the minimum number of forests that the edges of a graph can be partitioned into. Researchers have also studied linear k-arboricity, a variant of linear arboricity in which each path in the linear forest can have at most k edges. Another related problem is Hamiltonian decomposition, the problem of decomposing a regular graph of even degree Δ {\displaystyle \Delta } into exactly Δ / 2 {\displaystyle \Delta /2} Hamiltonian cycles. A given graph has a Hamiltonian decomposition if and only if the subgraph formed by removing an arbitrary vertex from the graph has linear arboricity Δ / 2 {\displaystyle \Delta /2} .

Computational complexity Unlike arboricity, which can be determined in polynomial time, linear arboricity is NP-hard. Even recognizing the graphs of linear arboricity two is NP-complete. However, for cubic graphs and other graphs of maximum degree three, the linear arboricity is always two, and a decomposition into two linear forests can be found in linear time using an algorithm based on depth-first search.

References

Illustrations

Linear arboricity: Partition of the graph of a rhombic dodecahedron into two linear forests, showing that its linear arboricity is two
Partition of the graph of a rhombic dodecahedron into two linear forests, showing that its linear arboricity is two

Worked examples

Example 1 — a first encounter with Linear arboricity

Start with the simplest possible case. Write down what Linear arboricity claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In science, 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 Linear arboricity 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 Linear arboricity 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 Linear arboricity

In research
Linear arboricity appears in science 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 Linear arboricity 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
Linear arboricity is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph invariants, NP-complete problems, so understanding it makes those chapters shorter.
In everyday life
Look for Linear arboricity 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 Linear arboricity in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Linear arboricity 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 Linear arboricity out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Linear arboricity in simple terms?

In graph theory, a branch of mathematics, the linear arboricity of an undirected graph is the smallest number of linear forests its edges can be partitioned into. Here, a linear forest is an acyclic graph with maximum degree two; that is, it is a disjoint union of path graphs.

Why does Linear arboricity matter?

Because it connects several science 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 Linear arboricity?

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 Linear arboricity.

Tags

  • Graph invariants
  • NP-complete problems

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