Recent Progress in Complex Network Analysis: Properties of Random Intersection Graphs
Experimental results show that in large complex networks (such as internet, social or biological networks) there exists a tendency to connect elements which have a common neighbor. In theoretical random graph models, this tendency is described by the clustering coefficient being bounded away from zero. Complex networks also have power-law degree distributions and short average distances (small world phenomena). These are desirable features of random graphs used for modeling real life networks. We survey recent results concerning various random intersection graph models showing that they have tunable clustering coefficient, a rich class of degree distributions including power-laws, and short average distances.
The work of M. Bloznelis and V. Kurauskas was supported by the Lithuanian Research Council (grant MIP-067/2013). J. Jaworski and K. Rybarczyk acknowledge the support by the National Science Centre (NCN)—DEC-2011/01/B/ST1/03943. Co-operation between E. Godehardt and J. Jaworski was also supported by Deutsche Forschungsgemeinschaft (grant no. GO 490/17-1).
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