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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Factorization threshold models for scale-free networks generation.

Akmal Artikov^1,2, Aleksandr Dorodnykh¹, Yana Kashinskaya^1,3

¹Moscow Institute of Physics and Technology (SU), Moscow, Russia.

Computational Social Networks

|January 23, 2018

Summary

This study introduces a novel method for generating scale-free networks using matrix factorization and geographical threshold models. The new approach offers an alternative to preferential attachment, producing networks with a power-law degree distribution.

Keywords:

Matrix factorization Scale-free networks Threshold models

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Area of Science:

Network Science
Complex Systems
Mathematical Modeling

Background:

Existing scale-free network models predominantly rely on preferential attachment.
There is a need for alternative methods to generate scale-free networks with different underlying mechanisms.

Purpose of the Study:

To propose a new model for generating scale-free networks.
To explore an alternative source for the power-law degree distribution in networks.

Main Methods:

The model integrates matrix factorization and geographical threshold concepts.
Nodes are represented by vectors with latent features on a unit sphere and Pareto-distributed weights.
Edges are formed based on spatial proximity and/or node weights.

Main Results:

The generated networks exhibit scale-free properties.
A power-law degree distribution with an exponent of 2 is achieved.
An extension allows for the generation of directed networks.

Conclusions:

The proposed model successfully generates scale-free networks.
The model provides a flexible framework for network generation.
Directed network generation with tunable exponents is feasible.