Network dynamics
Network dynamics is a research field for the study of networks whose status changes in time. The dynamics may refer to the structure of connections of the units of a network,[1][2] to the collective internal state of the network,[3] or both. The networked systems could be from the fields of biology, chemistry, physics, sociology, economics, computer science, etc. Networked systems are typically characterized as complex systems consisting of many units coupled by specific, potentially changing, interaction topologies.
The phase diagram of a model of a dynamic networks which include stability, non stability and metastabily regimes has been found in Majdandzik et al [1]
For a dynamical systems' approach to discrete network dynamics, see sequential dynamical system.
Connectedness
The connectedness of temporal networks can be studied by a mapping to the directed percolation problem.[4] The probability of temporal links has a critical value below which the connectedness is zero while above almost all nodes can be connected. This can be regarded as a phase transition [4]
Predictability
An entropy framework, based on combined topological and temporal regularities of links, has been developed for quantifying the predictability of temporal networks.[5]
See also
- Biological network inference
- Cellular neural network
- Dual-phase evolution
- Dynamic Bayesian network
- Dynamic network analysis
- Dynamic single-frequency networks
- Gaussian network model
- Gene regulatory network
- Gradient network
- Network planning and design
- Neural network
- Neurodynamics
- Small-world network
- Technology Dynamics
References
- ^ a b Majdandzic, A.; et al. (2014). "Spontaneous recovery in dynamical networks". Nature Physics. 10 (1): 34–38. Bibcode:2014NatPh..10...34M. doi:10.1038/nphys2819.
- ^ Jan Nagler; Anna Levina; Marc Timme (2011). "Impact of Single Links in Competitive Percolation". Nature Physics. 7 (3): 265–270. arXiv:1103.0922. Bibcode:2011NatPh...7..265N. doi:10.1038/nphys1860. S2CID 2809783.
- ^ John J. Hopfield (1982). "Neural networks and physical systems with emergent collective computational abilities". Proceedings of the National Academy of Sciences of the USA. 79 (8): 2554–2558. Bibcode:1982PNAS...79.2554H. doi:10.1073/pnas.79.8.2554. PMC 346238. PMID 6953413.
- ^ a b R. Parshani, M. Dickison, R. Cohen, H.E. Stanley, S. Havlin (2010). "Dynamic networks and directed". Europhys .Lett. 90 (3).
{{cite journal}}: CS1 maint: multiple names: authors list (link) - ^ D Tang, W Du, L Shekhtman, Y Wang, S Havlin, X Cao, G Yan (2000). "Predictability of real temporal networks". National Science Review. 7 (3): 937.
{{cite journal}}: More than one of|pages=and|page=specified (help)CS1 maint: multiple names: authors list (link)
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