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Temporal Ordering of Dynamic Expression Data from Detailed Spatial Expression Maps

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Constructing temporal networks with bursty activity patterns.

Anzhi Sheng^1,2, Qi Su^3,4,5, Aming Li^6,7

¹Center for Systems and Control, College of Engineering, Peking University, Beijing, 100871, China.

Nature Communications

|November 11, 2023

Summary

This summary is machine-generated.

Researchers developed a new spanning-tree method to model time-varying social interactions. This approach accurately reproduces the bursty patterns observed in real-world human behavior, offering insights into dynamic network processes.

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Area of Science:

Network Science
Computational Social Science
Statistical Physics

Background:

Human social interactions exhibit temporal variability in intensity, both online and offline.
Characterizing these dynamic interactions often involves analyzing networks with time-varying link and node activity.
Inter-event time distributions, measuring the duration between successive interactions, are frequently heavy-tailed in empirical data.

Purpose of the Study:

To develop a theoretical model capable of reproducing the burstiness observed in empirical temporal network data.
To construct temporal networks and activity patterns that exhibit bursty behavior.
To ensure the model can accommodate any desired inter-event time distributions for nodes and links.

Main Methods:

Development of a novel spanning-tree method for constructing temporal networks.
Incorporation of time-varying activity patterns within the network model.
Ensuring target inter-event time distributions are reproducible under a consistency condition.

Main Results:

The proposed spanning-tree method successfully generates temporal networks with bursty behavior.
The model can reproduce heavy-tailed inter-event time distributions observed in empirical datasets.
The method's effectiveness is independent of whether the underlying network topology is static or dynamic.

Conclusions:

The developed spanning-tree method provides a robust framework for modeling bursty interactions.
This model can accurately capture temporal dynamics seen in real-world human social networks.
It serves as a valuable tool for studying dynamic processes in systems characterized by bursty interactions.