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A Random Graph Model for Massive Graphs
, 2000
"... We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and loglog growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from the ..."
Abstract

Cited by 335 (26 self)
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We propose a random graph model which is a special case of sparse random graphs with given degree sequences. This model involves only a small number of parameters, called logsize and loglog growth rate. These parameters capture some universal characteristics of massive graphs. Furthermore, from these parameters, various properties of the graph can be derived. For example, for certain ranges of the parameters, we will compute the expected distribution of the sizes of the connected components which almost surely occur with high probability. We will illustrate the consistency of our model with the behavior of some massive graphs derived from data in telecommunications. We will also discuss the threshold function, the giant component, and the evolution of random graphs in this model.
A Random Graph Model for Power Law Graphs
 Experimental Math
, 2000
"... We propose a random graph m del which is a special case of sparse random graphs with given degree sequences which satisfy a power law. Thism odel involves only asm all num ber of param eters, called logsize and loglog growth rate. These param eters capturesom e universal characteristics ofm assive ..."
Abstract

Cited by 73 (4 self)
 Add to MetaCart
We propose a random graph m del which is a special case of sparse random graphs with given degree sequences which satisfy a power law. Thism odel involves only asm all num ber of param eters, called logsize and loglog growth rate. These param eters capturesom e universal characteristics ofm assive graphs. Furtherm re, from these paramfi ters, various properties of the graph can be derived. Forexam)(( for certain ranges of the paramJ?0CM we willcom?C7 the expected distribution of the sizes of the connectedcom onents which almJC surely occur with high probability. We will illustrate the consistency of our m del with the behavior of so m m ssive graphs derived from data in telecom unications. We will also discuss the threshold function, the giant com ponent, and the evolution of random graphs in thism del. 1