Background: 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. Methods: 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. Results and conclusion: 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.
CITATION STYLE
Artikov, A., Dorodnykh, A., Kashinskaya, Y., & Samosvat, E. (2016). Factorization threshold models for scale-free networks generation. Computational Social Networks, 3(1). https://doi.org/10.1186/s40649-016-0029-8
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