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Related Experiment Video

Updated: May 18, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Reverse preferential spread in complex networks.

Hiroshi Toyoizumi¹, Seiichi Tani, Naoto Miyoshi

¹Waseda University, Nishi-waseda 1-6-1, Shinjuku, Tokyo JP-169-8050, Japan.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics

|September 26, 2012

Summary

Reverse preferential spread, distributing information inversely proportional to node degree, proves more efficient than other methods in large networks. This approach maximizes information dissemination, achieving logistic growth independent of network structure.

Related Experiment Videos

Last Updated: May 18, 2026

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Soft Pneumatic Robot Modulates Graph Theory Metrics of Brain Network for Hand Rehabilitation After Stroke

Published on: October 10, 2025

Area of Science:

Network Science
Information Dissemination Dynamics

Background:

Large-degree nodes, while influential, can impede information spread due to redundant attempts.
Inefficient information diffusion can occur in complex networks.

Purpose of the Study:

To investigate the efficiency of reverse preferential spread for information dissemination.
To compare reverse preferential spread with other weight-based spread mechanisms.

Main Methods:

Theoretical analysis of information spread in large, uncorrelated networks.
Mathematical modeling of reverse preferential spread dynamics.

Main Results:

Reverse preferential spread demonstrates an advantage over other mechanisms.
The mean number of nodes reached follows logistic growth, independent of degree distribution.
This method provides an upper bound for information spread efficiency in weight-based mechanisms.

Conclusions:

Reverse preferential spread is a highly effective strategy for maximizing information dissemination in large networks.
The logistic growth pattern highlights the predictable and efficient nature of this spread mechanism.
Network structure, specifically degree distribution, does not limit the efficiency of reverse preferential spread.