# Random Graph Models

Chapter

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## Abstract

When the network properties of a real-world dataset is computed, we cannot determine from these values *per se* whether or not the results are surprising or expected. To be able to make these comparisons and judgements, we are in need of a null model whose values can be juxtaposed with the values of a real-world network. This juxtaposition helps us decide which properties are unexpected and require close examination, and which do not. One such null model is the random graph model. This chapter describes in detail the *Erdös–Rényi* random graph model and the *Bollobás* configuration random graph model along with some other models that can be used to generate random graphs. The properties of these graphs are also explained.

## References

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