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Path-based strong component algorithm

Path-based strong component algorithm is a computer 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 Path-based strong component algorithm rather than just read about it. In short: In graph theory, the strongly connected components of a directed graph may be found using an algorithm that uses depth-first search in combination with two stacks, one to keep track of the vertices in the current component and the second to keep track of the current search path. Versions of this algorithm have been proposed by Purdom (1970), Munro (1971), Dijkstra (1976), Cheriyan & Mehlhorn (1996), and Gabow (2000)…

Key takeaways

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

Reference excerpt

In graph theory, the strongly connected components of a directed graph may be found using an algorithm that uses depth-first search in combination with two stacks, one to keep track of the vertices in the current component and the second to keep track of the current search path. Versions of this algorithm have been proposed by Purdom (1970), Munro (1971), Dijkstra (1976), Cheriyan & Mehlhorn (1996), and Gabow (2000); of these, Dijkstra's version was the first to achieve linear time.

Description The algorithm performs a depth-first search of the given graph G, maintaining as it does two stacks S and P (in addition to the normal call stack for a recursive function). Stack S contains all the vertices that have not yet been assigned to a strongly connected component, in the order in which the depth-first search reaches the vertices. Stack P contains vertices that have not yet been determined to belong to different strongly connected components from each other. It also uses a counter C of the number of vertices reached so far, which it uses to compute the preorder numbers of the vertices. When the depth-first search reaches a vertex v, the algorithm performs the following steps:

Set the preorder number of v to C, and increment C. Push v onto S and also onto P. For each edge from v to a neighboring vertex w: If the preorder number of w has not yet been assigned (the edge is a tree edge), recursively search w; Otherwise, if w has not yet been assigned to a strongly connected component (the edge is a forward/back/cross edge): Repeatedly pop vertices from P until the top element of P has a preorder number less than or equal to the preorder number of w. If v is the top element of P: Pop vertices from S until v has been popped, and assign the popped vertices to a new component. Pop v from P. The overall algorithm consists of a loop through the vertices of the graph, calling this recursive search on each vertex that does not yet have a preorder number assigned to it.

Related algorithms Like this algorithm, Tarjan's strongly connected components algorithm also uses depth first search together with a stack to keep track of vertices that have not yet been assigned to a component, and moves these vertices into a new component when it finishes expanding the final vertex of its component. However, in place of the stack P, Tarjan's algorithm uses a vertex-indexed array of preorder numbers, assigned in the order that vertices are first visited in the depth-first search. The preorder array is used to keep track of when to form a new component.

Notes

References Cheriyan, J.; Mehlhorn, K. (1996), "Algorithms for dense graphs and networks on the random access computer", Algorithmica, 15 (6): 521–549, doi:10.1007/BF01940880, S2CID 8930091. Dijkstra, Edsger (1976), A Discipline of Programming, NJ: Prentice Hall, Ch. 25. Gabow, Harold N. (2000), "Path-based depth-first search for strong and biconnected components" (PDF), Information Processing Letters, 74 (3–4): 107–114, doi:10.1016/S0020-0190(00)00051-X, MR 1761551. Munro, Ian (1971), "Efficient determination of the transitive closure of a directed graph", Information Processing Letters, 1 (2): 56–58, doi:10.1016/0020-0190(71)90006-8. Purdom, P. Jr. (1970), "A transitive closure algorithm", BIT, 10: 76–94, doi:10.1007/bf01940892, S2CID 20818200. Sedgewick, R. (2004), "19.8 Strong Components in Digraphs", Algorithms in Java, Part 5 – Graph Algorithms (3rd ed.), Cambridge MA: Addison-Wesley, pp. 205–216.

Worked examples

Example 1 — a first encounter with Path-based strong component algorithm

Start with the simplest possible case. Write down what Path-based strong component algorithm claims or describes in one sentence, then invent the smallest concrete situation in which that sentence is true. In computer 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 Path-based strong component algorithm 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 Path-based strong component algorithm 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 Path-based strong component algorithm

In research
Path-based strong component algorithm appears in computer 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 Path-based strong component algorithm 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
Path-based strong component algorithm is common in secondary-school and first-year university syllabi. It links to neighbouring topics Graph algorithms, Graph connectivity, so understanding it makes those chapters shorter.
In everyday life
Look for Path-based strong component algorithm 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 Path-based strong component algorithm in 20 minutes

  1. Read the reference excerpt below once, without taking notes.
  2. Close the page and write down what Path-based strong component algorithm 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 Path-based strong component algorithm out loud to somebody else — or to Teacher Smith in the lgStudy chat.

Frequently asked questions

What is Path-based strong component algorithm in simple terms?

In graph theory, the strongly connected components of a directed graph may be found using an algorithm that uses depth-first search in combination with two stacks, one to keep track of the vertices in the current component and the second to keep track of the current search path. Versions of this al…

Why does Path-based strong component algorithm matter?

Because it connects several computer 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 Path-based strong component algorithm?

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 Path-based strong component algorithm.

Tags

  • Graph algorithms
  • Graph connectivity

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