Inferring Temporal Information from a Snapshot of a Dynamic Network
Jithin K Sreedharan1, Abram Magner2, Ananth Grama1
1Center for Science of Information, Department of Computer Science, Purdue University, West Lafayette, IN, USA.
Scientific Reports
|March 1, 2019
Summary
This study introduces network archaeology to reconstruct dynamic network evolution from a single snapshot. Methods infer node arrival order, revealing insights into infection spread, biomolecular networks, and brain connectome development.
Area of Science:
- Network science
- Computational biology
- Systems neuroscience
Background:
- Reverse-engineering dynamic network evolution (network archaeology) is crucial for understanding complex systems.
- Applications include infection spread, biomolecular interactions, economic flows, and human brain connectome evolution.
- Inferring node arrival order from a single network snapshot is a key challenge.
Purpose of the Study:
- To model, formulate, and analyze the problem of inferring node arrival order in dynamic networks from a single snapshot.
- To establish theoretical limits for the accuracy of such inferences.
- To develop and demonstrate practical methods for network evolution reconstruction.
Main Methods:
- Mathematical modeling and rigorous analysis of dynamic network structures.
- Derivation of theoretical bounds on the accuracy of inferring node ordering.
- Development of algorithms to approximate these bounds in practical scenarios.
Main Results:
- Theoretical limits on inferring node arrival order from static network snapshots were derived.
- Novel methods were developed that approach these theoretical limits.
- The methods demonstrated effectiveness across diverse applications, including social, citation, and brain networks.
Conclusions:
- Inferring the evolutionary history of dynamic networks from limited data is feasible.
- The developed methods provide a powerful tool for network archaeology.
- This work opens new avenues for studying the temporal dynamics of complex systems.
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