scholarly journals Moment-Based Parameter Estimation in Binomial Random Intersection Graph Models

Author(s):  
Joona Karjalainen ◽  
Lasse Leskelä
2008 ◽  
Vol 17 (4) ◽  
pp. 549-558 ◽  
Author(s):  
JERZY JAWORSKI ◽  
DUDLEY STARK

In a random passive intersection graph model the edges of the graph are decided by taking the union of a fixed number of cliques of random size. We give conditions for a random passive intersection graph model to have a limiting vertex degree distribution, in particular to have a Poisson limiting vertex degree distribution. We give related conditions which, in addition to implying a limiting vertex degree distribution, imply convergence of expectation.


2006 ◽  
Vol 306 (18) ◽  
pp. 2152-2165 ◽  
Author(s):  
Jerzy Jaworski ◽  
Michał Karoński ◽  
Dudley Stark

2011 ◽  
Vol 2011 ◽  
pp. 1-9 ◽  
Author(s):  
Yilun Shang

We study isolated vertices and connectivity in the random intersection graph . A Poisson convergence for the number of isolated vertices is determined at the threshold for absence of isolated vertices, which is equivalent to the threshold for connectivity. When and , we give the asymptotic probability of connectivity at the threshold for connectivity. Analogous results are well known in Erdős-Rényi random graphs.


10.37236/2786 ◽  
2013 ◽  
Vol 20 (3) ◽  
Author(s):  
Mindaugas Bloznelis ◽  
Julius Damarackas

We show the asymptotic degree distribution of the typical vertex of a sparse inhomogeneous random intersection graph.


10.37236/935 ◽  
2007 ◽  
Vol 14 (1) ◽  
Author(s):  
Michael Behrisch

We study the evolution of the order of the largest component in the random intersection graph model which reflects some clustering properties of real–world networks. We show that for appropriate choice of the parameters random intersection graphs differ from $G_{n,p}$ in that neither the so-called giant component, appearing when the expected vertex degree gets larger than one, has linear order nor is the second largest of logarithmic order. We also describe a test of our result on a protein similarity network.


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