Modeling Multi-Dimensional Datasets via a Fast Scale-Free Network Model

by   Shaojie Min, et al.

Compared with network datasets, multi-dimensional data are much more common nowadays. If we can model multi-dimensional datasets into networks with accurate network properties, while, in the meantime, preserving the original dataset features, we can not only explore the dataset dynamic but also acquire abundant synthetic network data. This paper proposed a fast scale-free network model for large-scale multi-dimensional data not limited to the network domain. The proposed network model is dynamic and able to generate scale-free graphs within linear time regardless of the scale or field of the modeled dataset. We further argued that in a dynamic network where edge-generation probability represents influence, as the network evolves, that influence also decays. We demonstrated how this influence decay phenomenon is reflected in our model and provided a case study using the Global Terrorism Database.


page 1

page 2

page 3

page 4


Optimal Multi-Dimensional Auctions: Conjectures and Simulations

We explore the properties of optimal multi-dimensional auctions in a mod...

Confluent-Drawing Parallel Coordinates: Web-Based Interactive Visual Analytics of Large Multi-Dimensional Data

Parallel coordinates plot is one of the most popular and widely used vis...

Sonifying stochastic walks on biomolecular energy landscapes

Translating the complex, multi-dimensional data from simulations of biom...

Finding Morton-Like Layouts for Multi-Dimensional Arrays Using Evolutionary Algorithms

The layout of multi-dimensional data can have a significant impact on th...

Multi-Dimensional, Multilayer, Nonlinear and Dynamic HITS

We introduce a ranking model for temporal multi-dimensional weighted and...

On the Incommensurability Phenomenon

Suppose that two large, multi-dimensional data sets are each noisy measu...

Please sign up or login with your details

Forgot password? Click here to reset