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Three-Dimensional Shape Modeling and Analysis of Brain Structures
Published on: November 14, 2019
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The shape of memory in temporal networks.
Oliver E Williams1, Lucas Lacasa2, Ana P Millán3,4
1School of Mathematical Sciences, Queen Mary University of London, London, E1 4NS, UK.
Nature Communications
|January 26, 2022
Summary
This study introduces a new mathematical framework to define and measure network memory, revealing diverse memory shapes across social, technological, and biological systems. Offline social networks exhibit particularly rich memory structures.
Area of Science:
- Complex systems science
- Network science
- Data science
Background:
- Defining and detecting network memory, the influence of a network's past on its structure, is an ongoing challenge.
- Temporal networks exhibit complex dependencies between link activities over time.
Purpose of the Study:
- To introduce a mathematical framework for defining and estimating the multidimensional memory shape of temporal networks.
- To characterize the intricate interplay between link activities within a network's history.
Main Methods:
- Development of a novel mathematical framework to quantify microscopic memory shape.
- Validation using synthetic network models.
- Analysis of real-world temporal networks from social, technological, and biological domains.
Main Results:
- Temporal network memory is inherently multidimensional.
- Real-world networks display heterogeneous memory shapes.
- Offline social networks show significantly richer memory and memory scales compared to online networks.
Conclusions:
- The proposed framework provides a robust method for analyzing network memory.
- The findings offer insights into the structural differences between online and offline social networks.
- The theory elucidates emergent phenomena like virtual loops and offers new tools for complex system exploration.
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