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Graph toughness

Graph toughness 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 Graph toughness rather than just read about it. In short: In graph theory, toughness is a measure of the connectivity of a graph. A graph G is said to be t-tough for a given real number t if, for every integer k > 1, G cannot be split into k different connected components by the removal of fewer than tk vertices.

Graph toughness — main illustration
Graph toughness — illustration

Key takeaways

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

Reference excerpt

In graph theory, toughness is a measure of the connectivity of a graph. A graph G is said to be t-tough for a given real number t if, for every integer k > 1, G cannot be split into k different connected components by the removal of fewer than tk vertices. For instance, a graph is 1-tough if the number of components formed by removing a set of vertices is always at most as large as the number of removed vertices. The toughness of a graph is the maximum t for which it is t-tough; this is a finite number for all finite graphs except the complete graphs, which by convention have infinite toughness. Graph toughness was first introduced by Václav Chvátal (1973). Since then there has been extensive work by other mathematicians on toughness; the recent survey by Bauer, Broersma & Schmeichel (2006) lists 99 theorems and 162 papers on the subject.

Examples Removing k vertices from a path graph can split the remaining graph into as many as k + 1 connected components. The maximum ratio of components to removed vertices is achieved by removing one vertex (from the interior of the path) and splitting it into two components. Therefore, paths are ⁠1/2⁠-tough. In contrast, removing k vertices from a cycle graph leaves at most k remaining connected components, and sometimes leaves exactly k connected components, so a cycle is 1-tough.

Connection to vertex connectivity If a graph is t-tough, then one consequence (obtained by setting k = 2) is that any set of 2t − 1 nodes can be removed without splitting the graph in two. That is, every t-tough graph is also 2t-vertex-connected.

Connection to Hamiltonicity

Chvátal (1973) observed that every cycle, and therefore every Hamiltonian graph, is 1-tough; that is, being 1-tough is a necessary condition for a graph to be Hamiltonian. He conjectured that the connection between toughness and Hamiltonicity goes in both directions: that there exists a threshold t such that every t-tough graph is Hamiltonian. Chvátal's original conjecture that t = 2 would have proven Fleischner's theorem but was disproved by Bauer, Broersma & Veldman (2000). The existence of a larger toughness threshold for Hamiltonicity remains open, and is sometimes called Chvátal's toughness conjecture.

Computational complexity Testing whether a graph is 1-tough is co-NP-complete. That is, the decision problem whose answer is "yes" for a graph that is not 1-tough, and "no" for a graph that is 1-tough, is NP-complete. The same is true for any fixed positive rational number q: testing whether a graph is q-tough is co-NP-complete (Bauer, Hakimi & Schmeichel 1990).

See also Strength of a graph, an analogous concept for edge deletions Tutte–Berge formula, a related characterization of the size of a maximum matching in a graph Harris graphs, a family of graphs that are tough, Eulerian, and non-Hamiltonian

References Bauer, Douglas; Broersma, Hajo; Schmeichel, Edward (2006), "Toughness in graphs—a survey", Graphs and Combinatorics, 22 (1): 1–35, doi:10.1007/s00373-006-0649-0, MR 2221006, S2CID 3237188. Bauer, D.; Broersma, H. J.; Veldman, H. J. (2000), "Not every 2-tough graph is Hamiltonian", Proceedings of the 5th Twente Workshop on Graphs and Combinatorial Optimization (Enschede, 1997), Discrete Applied Mathematics, 99 (1–3) (1-3 ed.): 317–321, doi:10.1016/S0166-218X(99)00141-9, MR 1743840. Bauer, D.; Hakimi, S. L.; Schmeichel, E. (1990), "Recognizing tough graphs is NP-hard", Discrete Applied Mathematics, 28 (3): 191–195, doi:10.1016/0166-218X(90)90001-S, MR 1074858. Chvátal, Václav (1973), "Tough graphs and Hamiltonian circuits", Discrete Mathematics, 5 (3): 215–228, doi:10.1016/0012-365X(73)90138-6, MR 0316301.

Illustrations

Graph toughness: In this graph, removing the four red vertices would produce four connected components (depicted in four different colours). However, there is no set of k vertices whose removal leaves more than k components. Therefore, its toughness is exactly 1.
In this graph, removing the four red vertices would produce four connected components (depicted in four different colours). However, there is no set of k vertices whose removal leaves more than k components. Therefore, its toughness is exactly 1.

Worked examples

Example 1 — a first encounter with Graph toughness

Start with the simplest possible case. Write down what Graph toughness 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 Graph toughness 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 toughness 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 toughness

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

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

Frequently asked questions

What is Graph toughness in simple terms?

In graph theory, toughness is a measure of the connectivity of a graph. A graph G is said to be t-tough for a given real number t if, for every integer k > 1, G cannot be split into k different connected components by the removal of fewer than tk vertices.

Why does Graph toughness 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 Graph toughness?

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 toughness.

Tags

  • Graph connectivity
  • Graph invariants
  • NP-complete problems

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