A critical point for random graphs with a given degree sequence
Random Graphs 93 Proceedings of the sixth international seminar on Random graphs and probabilistic methods in combinatorics and computer science
On Random Intersection Graphs: The Subgraph Problem
Combinatorics, Probability and Computing
The Size of the Giant Component of a Random Graph with a Given Degree Sequence
Combinatorics, Probability and Computing
The vertex degree distribution of random intersection graphs
Random Structures & Algorithms - Isaac Newton Institute Programme “Computation, Combinatorics and Probability”: Part I
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
Evolution of Networks: From Biological Nets to the Internet and WWW (Physics)
The phase transition in inhomogeneous random graphs
Random Structures & Algorithms
On the scale-free intersection graphs
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
Component Evolution in General Random Intersection Graphs
SIAM Journal on Discrete Mathematics
On the independence number and Hamiltonicity of uniform random intersection graphs
Theoretical Computer Science
Constructions of independent sets in random intersection graphs
Theoretical Computer Science
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A random intersection graph is constructed by assigning independently to each vertex a subset of a given set and drawing an edge between two vertices if and only if their respective subsets intersect. In this article a model is developed in which each vertex is given a random weight and vertices with larger weights are more likely to be assigned large subsets. The distribution of the degree of a given vertex is characterized and is shown to depend on the weight of the vertex. In particular, if the weight distribution is a power law, the degree distribution will be as well. Furthermore, an asymptotic expression for the clustering in the graph is derived. By tuning the parameters of the model, it is possible to generate a graph with arbitrary clustering, expected degree, and—in the power-law case—tail exponent.