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Related Experiment Video

Updated: Jul 3, 2025

Modeling the Functional Network for Spatial Navigation in the Human Brain

Modeling the Functional Network for Spatial Navigation in the Human Brain

Published on: October 13, 2023

The distance backbone of complex networks.

Tiago Simas¹, Rion Brattig Correia², Luis M Rocha³

¹Departamento de Engenharia Informtáica e Sistemas de Informação, Universidade Lusófona, Lisboa, Portugal.

Journal of Complex Networks

|February 13, 2024

Summary

Redundancy is key to network robustness. This study introduces a distance backbone subgraph to precisely characterize network interactions and identify essential paths, revealing vast redundancy in complex systems.

Keywords:

Complex systems Network backbones Redundancy Shortest paths Transitivity

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Area of Science:

Network Science
Graph Theory
Complexity Science

Background:

Redundancy is crucial for network evolution and robustness.
Precise characterization of redundancy in multivariate interaction networks is needed.
Understanding network dynamics requires analyzing path lengths and connectivity.

Purpose of the Study:

To develop a method for characterizing redundancy in weighted graphs.
To infer connection transitivity and identify shortest paths.
To provide a principled graph reduction technique for analyzing network geometry.

Main Methods:

Inferred connection transitivity for weighted graphs.
Computed all possible measures of path length.
Derived a distance backbone subgraph for efficient shortest path computation.

Main Results:

Developed a graph reduction technique yielding a distance backbone subgraph.
The distance backbone is sufficient to compute all shortest paths.
Demonstrated that the distance backbone is small in large-scale networks across various domains.

Conclusions:

Network robustness stems from significant redundancy.
The distance backbone subgraph offers a finer characterization of network geometry.
The findings are applicable to understanding spread and communication in real-world networks.