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Modeling the Functional Network for Spatial Navigation in the Human Brain

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Published on: October 13, 2023

Hub-collision avoidance and leaf-node options algorithm for fractal dimension and renormalization of complex

Fei-Yan Guo¹, Jia-Jun Zhou², Zhong-Yuan Ruan²

¹School of Economics and Management, Beijing Information Science and Technology University, Beijing 100192, China.

Chaos (Woodbury, N.Y.)

|January 1, 2023

Summary

This summary is machine-generated.

The new hub-collision avoidance and leaf-node options (HALO) algorithm improves box-covering analysis for complex networks. HALO offers superior performance and accuracy in fractal dimension estimation compared to existing methods.

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Area of Science:

Complex networks analysis
Fractal geometry
Network science

Background:

The box-covering method is crucial for understanding complex network properties.
Existing methods face challenges with randomness and hub structures.

Purpose of the Study:

Introduce the hub-collision avoidance and leaf-node options (HALO) algorithm.
Enhance fractal property recognition and renormalization analysis in complex networks.

Main Methods:

Developed bidirectional network traversal with forward (hub-collision avoidance) and reverse (leaf-node preferential selection) sampling rules.
Implemented a box selection process prioritizing larger boxes by removing smaller ones.
Compared HALO against CBB, MEMB, OBCA, and SM30 algorithms on nine real networks.

Main Results:

HALO achieved the highest performance, using 11.40% to 8.19% fewer boxes than compared algorithms.
Demonstrated significantly improved algorithm determinism and more accurate fractal dimension estimations.
Showcased stable performance across different networks, unaffected by hub tightness, unlike MEMB or OBCA.

Conclusions:

HALO offers a more efficient and accurate approach to box-covering analysis in complex networks.
The algorithm's robustness and improved determinism make it a valuable tool for network analysis.
HALO provides a reliable method for fractal dimension estimation, outperforming existing techniques.