Multilayer Network Neuroscience: Decoding the Brain’s Complex Systems
#NetworkNeuroscience #BrainConnectivity #Connectome #Neuroscience #BrainMapping #GraphTheory #MultilayerNetworks #BrainResearch #Neurotechnology #PersonalizedMedicine #BrainHealth #CognitiveScience #Neuroimaging #BrainScience
Several theoretical frameworks have been proposed to formally represent #MultilayerNetworks.
Here are some of the main theoretical references:
1. De Domenico et al. (2013) https://doi.org/10.1103/PhysRevX.3.041022;
2. Kivelä et al. (2014) https://doi.org/10.1093/comnet/cnu016;
3. Aleta and Moreno (2019) https://doi.org/10.1146/annurev-conmatphys-031218-013259;
4. Bianconi (2018) https://doi.org/10.1093/oso/9780198753919.001.0001;
5. Cozzo et al. (2018) https://doi.org/10.1007/978-3-319-92255-3;
6. Artime et al. (2022) https://doi.org/10.1017/9781009085809;
7. De Domenico (2022) https://doi.org/10.1007/978-3-030-75718-2.
A ``monoplex'' network, like a Facebook-based social network, can be represented by a set of nodes (people) linked by their Facebook connections (interactions). But real-world networks can be ``multiplex,'' with multiple types of interactions and where one type of interaction can influence another. A unifying framework for describing ``multiplex'' networks has been missing so far. Deftly employing the concept of tensors, theorists now present such a framework that will power studies of ``multiplex'' networks across many scientific disciplines.
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