Abstract
The graphs models we have discussed upto this point cater to specific network properties. In this chapter, we will discuss Kronecker graphs which are capable of generating a wide-array of properties. Kronecker graphs are generated by successively multiplying an initiator graph. This chapter looks at the properties of these Kronecker graphs. The chapter will look at stochastic Kronecker graphs (SKG), which eliminates features such as the “staircase effect”. Several techniques used to generate these SKGs will also be covered. However, SKGs are unable to generate the required power-law or lognormal distribution. To enable this, noisy stochastic Kronecker graphs (NSKG) will be discussed. We will then look at distance-dependent Kronecker graphs that enable searchability and several algorithms that can generate these Kronecker graphs.
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Raj P. M., K., Mohan, A., Srinivasa, K.G. (2018). Kronecker Graphs. In: Practical Social Network Analysis with Python. Computer Communications and Networks. Springer, Cham. https://doi.org/10.1007/978-3-319-96746-2_12
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DOI: https://doi.org/10.1007/978-3-319-96746-2_12
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