science

GraphCrunch

GraphCrunch 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 GraphCrunch rather than just read about it. In short: GraphCrunch is a comprehensive, parallelizable, and easily extendible open source software tool for analyzing and modeling large biological networks (or graphs); it compares real-world networks against a series of random graph models with respect to a multitude of local and global network properties. Motivation Recent technological advances in experimental biology have yielded large amounts of biological network dat…

Key takeaways

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

Reference excerpt

GraphCrunch is a comprehensive, parallelizable, and easily extendible open source software tool for analyzing and modeling large biological networks (or graphs); it compares real-world networks against a series of random graph models with respect to a multitude of local and global network properties.

Motivation Recent technological advances in experimental biology have yielded large amounts of biological network data. Many other real-world phenomena have also been described in terms of large networks (also called graphs), such as various types of social and technological networks. Thus, understanding these complex phenomena has become an important scientific problem that has led to intensive research in network modeling and analyses. An important step towards understanding biological networks is finding an adequate network model. Evaluating the fit of a model network to the data is a formidable challenge, since network comparisons are computationally infeasible and thus have to rely on heuristics, or "network properties." GraphCrunch automates the process of generating random networks drawn from a series of random graph models and evaluating the fit of the network models to a real-world network with respect to a variety of global and local network properties.

Features GraphCrunch performs the following tasks: 1) computes user specified global and local properties of an input real-world network, 2) creates a user specified number of random networks belonging to user specified random graph models, 3) compares how closely each model network reproduces a range of global and local properties (specified in point 1 above) of the real-world network, and 4) produces the statistics of network property similarities between the data and the model networks.

Network models supported by GraphCrunch GraphCrunch currently supports five different types of random graph models:

Erdös-Rényi random graphs; random graphs with the same degree distribution as the data; Barabási-Albert preferential-attachment scale-free networks; n-dimensional geometric random graphs (for all positive integers n); and stickiness model networks.

Network properties supported by GraphCrunch GraphCrunch currently supports seven global and local network properties:

degree distribution; clustering coefficient; clustering spectrum; average diameter; spectrum of shortest path lengths; relative graphlet frequency distance; and graphlet degree distribution agreement.

Applications GraphCrunch has been used to find an optimal network model for protein-protein interaction networks, as well as for protein structure networks.

References

External links http://bio-nets.doc.ic.ac.uk/graphcrunch2/

Worked examples

Example 1 — a first encounter with GraphCrunch

Start with the simplest possible case. Write down what GraphCrunch 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 GraphCrunch 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 GraphCrunch 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 GraphCrunch

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

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

Frequently asked questions

What is GraphCrunch in simple terms?

Why does GraphCrunch 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 GraphCrunch?

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 GraphCrunch.