The fundamental advantages of temporal networks

By Aming Li, Sean P. Cornelius, Yang-Yu Liu, Long Wang, Albert-László Barabási. Science, 2016

420 citations recorded by the source corpus.

In network science, change is good Historically, network science focused on static networks, in which nodes are connected by permanent links. However, in networked systems ranging from protein-protein interactions to social networks, links change. Although it might seem that permanent links would make it easier to control a system, Li et al. demonstrate that temporality has advantages in real and simulated networks. Temporal networks can be controlled more efficiently and require less energy than their static counterparts. Science, this issue p. 1042 In control networks, when links between nodes are not permanent, there are benefits to the system. Most networked systems of scientific interest are characterized by temporal links, meaning the network’s structure changes over time. Link temporality has been shown to hinder many dynamical processes, from information spreading to accessibility, by disrupting network paths. Considering the ubiquity of temporal networks in nature, we ask: Are there any advantages of the networks’ temporality? We use an analytical framework to show that temporal networks can, compared to their static counterparts, reach controllability faster, demand orders of magnitude less control energy, and have control trajectories, that are considerably more compact than those characterizing static networks. Thus, temporality ensures a degree of flexibility that would be unattainable in static networks, enhancing our ability to control them.

Subfields: Computer Networks and Communications, Statistical and Nonlinear Physics

View Paper: https://www.semanticscholar.org/paper/dfffaa3e4c3b035299c416eb5e602d922a5ad10c

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In Computer Networks and Communications

In Statistical and Nonlinear Physics

Citations in the corpus

Bots increase exposure to negative and inflammatory content in online social systems (2018, neutral): “This concerning trend, coupled with the emerging ability to control time-varying networks such as online social systems (32), further motivates the crucial need for the development of quantitative techniques like the one proposed here for unmasking the social manipulation enacted by bots.”
Network control principles predict neuron function in the Caenorhabditis elegans connectome (2017, background): “It is also theoretically possible to adapt our tools to temporally varying sensory inputs and behavioural responses invoking a framework to control temporal networks27–29.”
The time-evolving epileptic brain network: concepts, definitions, accomplishments, perspectives (2024, background): “…decades have seen quite a number of accomplishments in de ﬁ ning and characterizing the epileptic brain network, including an important change in perspective from a static to a time-evolving network (cf. Li et al., 2017), which better takes into account the fact that epilepsy is a dynamic disease.”