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Hierarchical closeness

Hierarchical closeness 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 Hierarchical closeness rather than just read about it. In short: Hierarchical closeness (HC) is a structural centrality measure used in network theory or graph theory. It is extended from closeness centrality to rank how centrally located a node is in a directed network.

Key takeaways

  • Hierarchical closeness 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 Hierarchical closeness to a quantity you can measure, compute or draw — that is where exam questions come from.
  • Reproduce the core statement of Hierarchical closeness from memory before moving on to harder problems.

Reference excerpt

Hierarchical closeness (HC) is a structural centrality measure used in network theory or graph theory. It is extended from closeness centrality to rank how centrally located a node is in a directed network. While the original closeness centrality of a directed network considers the most important node to be that with the least total distance from all other nodes, hierarchical closeness evaluates the most important node as the one which reaches the most nodes by the shortest paths. The hierarchical closeness explicitly includes information about the range of other nodes that can be affected by the given node. In a directed network G ( V , A ) {\displaystyle G(V,A)} where V {\displaystyle V} is the set of nodes and A {\displaystyle A} is the set of interactions, hierarchical closeness of a node i {\displaystyle i} ∈ V {\displaystyle V} called C h c ( i ) {\displaystyle C_{hc}(i)} was proposed by Tran and Kwon as follows:

C h c ( i ) = N R ( i ) + C ( c l o − i ) ( i ) {\displaystyle C_{hc}(i)=N_{R}(i)+C_{(clo-i)}(i)}

where:

N R ( i ) ∈ [ 0 , | V | − 1 ] {\displaystyle N_{R}(i)\in [0,|V|-1]} is the reachability of a node i {\displaystyle i} defined by N R ( i ) = | { j ∈ V : ∃ {\displaystyle N_{R}(i)=|\{j\in V:\exists } a path from i {\displaystyle i} to j } | {\displaystyle j\}|} , and

C c l o ( i ) {\displaystyle C_{clo}(i)} is the normalized form of original closeness (Sabidussi, 1966). It can use a variant definition of closeness as follows: C c l o − i ( i ) = 1 | V | − 1 ∑ j ∈ V ∖ { i } 1 d ( i , j ) {\displaystyle C_{clo-i}(i)={\frac {1}{|V|-1}}\sum _{j\in V\setminus \{i\}}{\frac {1}{d(i,j)}}} where d ( i , j ) {\displaystyle d(i,j)} is the distance of the shortest path, if any, from i {\displaystyle i} to j {\displaystyle j} ; otherwise, d ( i , j ) {\displaystyle d(i,j)} is specified as an infinite value. In the formula, N R ( i ) {\displaystyle N_{R}(i)} represents the number of nodes in V {\displaystyle V} that can be reachable from i {\displaystyle i} . It can also represent the hierarchical position of a node in a directed network. It notes that if N R ( i ) = 0 {\displaystyle N_{R}(i)=0} , then C h c ( i ) = 0 {\displaystyle C_{hc}(i)=0} because C ( c l o − i ) ( i ) {\displaystyle C_{(clo-i)}(i)} is 0 {\displaystyle 0} . In cases where N R ( i ) > 0 {\displaystyle N_{R}(i)>0} , the reachability is a dominant factor because N R ( i ) ≥ 1 {\displaystyle N_{R}(i)\geq 1} but C ( c l o − i ) ( i ) < 1 {\displaystyle C_{(clo-i)}(i)<1} . In other words, the first term indicates the level of the global hierarchy and the second term presents the level of the local centrality.

Application Hierarchical closeness can be used in biological networks to rank the risk of genes to carry diseases.[1]

References

Worked examples

Example 1 — a first encounter with Hierarchical closeness

Start with the simplest possible case. Write down what Hierarchical closeness 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 Hierarchical closeness 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 Hierarchical closeness 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 Hierarchical closeness

In research
Hierarchical closeness 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 Hierarchical closeness 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
Hierarchical closeness is common in secondary-school and first-year university syllabi. It links to neighbouring topics Algebraic graph theory, Graph algorithms, Graph theory, so understanding it makes those chapters shorter.
In everyday life
Look for Hierarchical closeness 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 Hierarchical closeness in 20 minutes

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

Frequently asked questions

What is Hierarchical closeness in simple terms?

Hierarchical closeness (HC) is a structural centrality measure used in network theory or graph theory. It is extended from closeness centrality to rank how centrally located a node is in a directed network.

Why does Hierarchical closeness 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 Hierarchical closeness?

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 Hierarchical closeness.

Tags

  • Algebraic graph theory
  • Graph algorithms
  • Graph theory
  • Network analysis
  • Network theory
  • Networks

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