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- W2949969006 abstract "Real social networks are often compared to random graphs in order to assess whether their typological structure could be the result of random processes. However, an ErdH{o}s-R'enyi random graph in large scale is often lack of local structure beyond the dyadic level and as a result we need to generate the clustered random graph instead of the simple random graph to compare the local structure at the triadic level. In this paper a generalized version of Gleeson's algorithm is advanced to generate a clustered random graph in large-scale which persists the number of nodes |V|, the number of edges |E|, and the global clustering coefficient C{Delta} as in the real network. And it also has advantages in randomness evaluation and computation time when comparing with the existing algorithms." @default.
- W2949969006 created "2019-06-27" @default.
- W2949969006 creator A5091782753 @default.
- W2949969006 date "2013-01-28" @default.
- W2949969006 modified "2023-10-06" @default.
- W2949969006 title "Algorithms for Generating Large-scale Clustered Random Graphs" @default.
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- W2949969006 doi "https://doi.org/10.48550/arxiv.1301.6802" @default.
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