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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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Published on: February 9, 2017

Predictability of real temporal networks.

Disheng Tang^1,2,3, Wenbo Du^1,3, Louis Shekhtman⁴

¹School of Electronic and Information Engineering, Beihang University, Beijing 100191, China.

National Science Review

|October 25, 2021

Summary

This summary is machine-generated.

Predicting temporal network changes is complex. This study introduces a new framework combining network structure and time, showing it improves prediction accuracy for real-world networks.

Keywords:

network entropy predictability predictive algorithm temporal network

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Area of Science:

Network Science
Complex Systems
Data Science

Background:

Real-world networks exhibit dynamic links over time.
Link temporality influences network dynamics and function.
Predicting temporal link patterns is challenging due to topological-temporal entanglement.

Purpose of the Study:

To develop a framework for quantifying temporal network predictability.
To integrate both topological and temporal network regularities for improved prediction.
To assess the predictability of various real-world temporal networks.

Main Methods:

Proposed an entropy-rate-based framework.
Combined topological and temporal regularities for predictability quantification.
Applied the framework to model and 18 real-world networks.

Main Results:

The framework captures intrinsic topological-temporal regularities.
Combined topological-temporal predictability often exceeds temporal predictability alone.
Demonstrated higher predictability in most real temporal networks by integrating both aspects.

Conclusions:

Incorporating both temporal and topological network aspects is crucial for accurate predictions.
The proposed framework offers a robust method for assessing temporal network predictability.
Findings highlight the interconnectedness of network structure and temporal dynamics.