Reconstruction of stochastic temporal networks through diffusive arrival times
1Adaptive Networks and Control Laboratory, Department of Electronic Engineering, and Research Center of Smart Networks and Systems, School of Information Science and Engineering, Fudan University, Shanghai 200433, China.
Nature Communications
|June 13, 2017
Summary
This study introduces an inverse modeling method to infer temporal networks from diffusion data. The approach accurately reconstructs complex interaction patterns using first-arrival observations, even with limited data.
Area of Science:
- Complex Systems Science
- Network Science
- Data Science
Background:
- Temporal networks offer new ways to understand complex systems.
- Limited individual-level data hinders the study of time-resolved interactions.
- Reconstructing dynamic network structures remains a challenge.
Purpose of the Study:
- To develop an inverse modeling method for inferring temporal networks.
- To enable network reconstruction from diffusion process observations.
- To address limitations in accessing empirical temporal network data.
Main Methods:
- Proposed an inverse modeling approach using first-arrival observations of diffusion.
- Implemented an efficient coordinate-ascent algorithm for inference.
- Utilized a null model assumption of mutually independent interaction sequences.
Main Results:
- Validated the algorithm on synthesized and empirical network datasets.
- Demonstrated statistically accurate inference of temporal networks from moderate diffusion cascade samples.
- Showcased the feasibility of reconstructing temporal networks.
Conclusions:
- The proposed method effectively infers stochastic temporal networks.
- This approach offers a flexible scheme for temporally augmented network reconstruction.
- The method has broad potential applications in various fields.
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