Factorization threshold models for scale-free networks generation.

Q1 Mathematics

Computational Social Networks Pub Date : 2016-01-01 Epub Date: 2016-08-22 DOI:10.1186/s40649-016-0029-8

Akmal Artikov, Aleksandr Dorodnykh, Yana Kashinskaya, Egor Samosvat

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引用次数: 2

Abstract

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.

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无标度网络生成的分解阈值模型。

背景:已经提出了几种产生无标度网络的模型;它们大多是基于优先依恋方法。在这篇文章中，我们提出了一种新的方法来产生无标度网络与一个替代源的幂律度分布。方法:该模型来源于矩阵分解方法和地理阈值模型，这些方法最近被证明在生成无标度网络方面表现出良好的效果。我们将每个节点与具有分布在单位球体上的潜在特征的向量以及从帕累托分布中采样的权重变量相关联。如果两个节点在空间上接近或权重较大，我们就用一条边连接它们。结果与结论:该方法产生的网络是无标度的，具有指数为2的幂律度分布。此外，我们提出了模型的扩展，使我们能够生成具有可调幂律指数的有向网络。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Computational Social Networks Mathematics-Modeling and Simulation

自引率

0.00%

发文量

审稿时长

13 weeks

期刊介绍： Computational Social Networks showcases refereed papers dealing with all mathematical, computational and applied aspects of social computing. The objective of this journal is to advance and promote the theoretical foundation, mathematical aspects, and applications of social computing. Submissions are welcome which focus on common principles, algorithms and tools that govern network structures/topologies, network functionalities, security and privacy, network behaviors, information diffusions and influence, social recommendation systems which are applicable to all types of social networks and social media. Topics include (but are not limited to) the following: -Social network design and architecture -Mathematical modeling and analysis -Real-world complex networks -Information retrieval in social contexts, political analysts -Network structure analysis -Network dynamics optimization -Complex network robustness and vulnerability -Information diffusion models and analysis -Security and privacy -Searching in complex networks -Efficient algorithms -Network behaviors -Trust and reputation -Social Influence -Social Recommendation -Social media analysis -Big data analysis on online social networks This journal publishes rigorously refereed papers dealing with all mathematical, computational and applied aspects of social computing. The journal also includes reviews of appropriate books as special issues on hot topics.