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A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

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Published on: March 25, 2014

Higher-order link prediction via local information.

Bo Liu^1,2, Rongmei Yang¹, Linyuan Lü^1,2,3

¹Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610054, People's Republic of China.

Chaos (Woodbury, N.Y.)

|August 3, 2023

Summary

This summary is machine-generated.

This study introduces two new methods, simplicial decomposition weight and closed ratio weight, for predicting future higher-order interactions in complex networks. These local feature-based approaches outperform existing benchmarks for higher-order link prediction.

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A Protocol for Computer-Based Protein Structure and Function Prediction

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Published on: November 3, 2011

Area of Science:

Network Science
Complex Systems Analysis
Data Mining

Background:

Higher-order link prediction is crucial for accurately modeling complex systems.
Traditional pairwise networks lack the detail of higher-order networks.
Predicting higher-order links is challenging due to network complexity.

Purpose of the Study:

To develop efficient and concise higher-order link prediction algorithms using local features.
To introduce novel similarity metrics for predicting future higher-order interactions (simplices) in simplicial networks.

Main Methods:

Proposed two similarity metrics: simplicial decomposition weight and closed ratio weight.
These metrics capture local higher-order information through simplex decomposition and clique states.
Evaluated performance on eight empirical simplicial networks.

Main Results:

The proposed metrics outperform existing benchmarks in predicting third-order and fourth-order interactions.
The algorithms demonstrate robust performance across various training set sizes.
Local features prove advantageous for higher-order link prediction.

Conclusions:

The novel metrics provide an effective approach for higher-order link prediction.
The findings highlight the importance of local features in complex network analysis.
The proposed algorithms offer a promising direction for future research in network science.