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Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Network Analysis of the Default Mode Network Using Functional Connectivity MRI in Temporal Lobe Epilepsy

Published on: August 5, 2014

Dynamic topologies of activity-driven temporal networks with memory.

Hyewon Kim¹, Meesoon Ha², Hawoong Jeong^1,3

¹Department of Physics, Korea Advanced Institute of Science and Technology, Daejeon 34141, Korea.

Physical Review. E

|July 18, 2018

Summary

We introduce dynamic scaling for temporal networks with memory and varying activity. Our findings reveal how time resolution and memory influence network topology and diffusion, impacting scaling properties.

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Area of Science:

Complex Systems
Network Science
Statistical Physics

Background:

Temporal networks exhibit dynamic topologies that evolve over time.
Understanding diffusion and scaling properties in these networks is crucial.
Existing models often simplify network dynamics, neglecting memory effects.

Purpose of the Study:

To propose and analyze dynamic scaling in temporal networks with heterogeneous activities and memory.
To investigate the interplay between time resolution and memory on network topology.
To explore diffusion properties and topological changes using a random-walk process.

Main Methods:

Modified activity-driven network model incorporating heterogeneous activities and memory.
Random-walk (RW) simulations to study diffusion and topological changes.
Temporal percolation theory to derive scaling exponents for the largest cluster and RW coverage.

Main Results:

Time resolution determines the effective network size, while memory influences scaling at the dynamic-to-static crossover.
Memory-dependent scaling originates from the largest cluster's dynamics, linked to temporal degree distributions.
Numerical confirmation of an extended finite-size scaling ansatz for dynamic topologies.

Conclusions:

Dynamic scaling provides a framework for understanding temporal network topologies.
Memory is a key factor affecting scaling properties and network dynamics.
The proposed scaling ansatz offers fundamental insights into temporal network behavior.