Extracting the signed backbone of intrinsically dense weighted networks

12/09/2020
by   Furkan Gursoy, et al.
0

Networks provide useful tools for analyzing diverse complex systems from natural, social, and technological domains. Growing size and variety of data such as more nodes and links and associated weights, directions, and signs can provide accessory information. Link and weight abundance, on the other hand, results in denser networks with noisy, insignificant, or otherwise redundant data. Moreover, typical network analysis and visualization techniques presuppose sparsity and are not appropriate or scalable for dense and weighted networks. As a remedy, network backbone extraction methods aim to retain only the important links while preserving the useful and elucidative structure of the original networks for further analyses. Here, we provide the first methods for extracting signed network backbones from intrinsically dense unsigned weighted networks. Utilizing a null model based on statistical techniques, the proposed significance filter and vigor filter allow inferring edge signs. Empirical analysis on migration, voting, temporal interaction, and species similarity networks reveals that the proposed filters extract meaningful and sparse signed backbones while preserving the multiscale nature of the network. The resulting backbones exhibit characteristics typically associated with signed networks such as reciprocity, structural balance, and community structure.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
05/26/2021

Block Dense Weighted Networks with Augmented Degree Correction

Dense networks with weighted connections often exhibit a community like ...
research
09/06/2023

Controllability Backbone in Networks

This paper studies the controllability backbone problem in dynamical net...
research
03/22/2017

Randomizing growing networks with a time-respecting null model

Complex networks are often used to represent systems that are not static...
research
01/15/2021

Visualizing and Interacting with Geospatial Networks: A Survey and Design Space

This paper surveys visualization and interaction techniques for geospati...
research
08/22/2019

ChordLink: A New Hybrid Visualization Model

Many real-world networks are globally sparse but locally dense. Typical ...
research
12/08/2019

The Probabilistic Backbone of Data-Driven Complex Networks: An example in Climate

Correlation Networks (CNs) inherently suffer from redundant information ...
research
04/06/2021

Inferring Network Structures via Signal Lasso

Inferring the connectivity structure of networked systems from data is a...

Please sign up or login with your details

Forgot password? Click here to reset