Title |
Factorization threshold models for scale-free networks generation
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Published in |
Computational Social Networks, August 2016
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DOI | 10.1186/s40649-016-0029-8 |
Pubmed ID | |
Authors |
Akmal Artikov, Aleksandr Dorodnykh, Yana Kashinskaya, Egor Samosvat |
Abstract |
Several models for producing scale-free networks have been suggested; most of them are based on the preferential attachment approach. In this article, we suggest a new approach for generating scale-free networks with an alternative source of the power-law degree distribution. The model derives from matrix factorization methods and geographical threshold models that were recently proven to show good results in generating scale-free networks. We associate each node with a vector having latent features distributed over a unit sphere and with a weight variable sampled from a Pareto distribution. We join two nodes by an edge if they are spatially close and/or have large weights. The network produced by this approach is scale free and has a power-law degree distribution with an exponent of 2. In addition, we propose an extension of the model that allows us to generate directed networks with tunable power-law exponents. |
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