
For the modeling of large networks a random graph is proposed, the generation of which is based on the use of nonlinear preferential attachment rule. The analytical solution for the vertex degree distribution is given as well as the method for the calibration of graph generator according to the node degree distributions in real networks. Possibilities of structural identification are demonstrated using the proposed graph for a network of actors acted the same films, the Internet, networks of roads, networks of complementary goods in online store.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 2 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
