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Contribution Details
| Type | Conference Presentation |
| Scope | Discipline-based scholarship |
| Title | Network volatility as a source of collective dynamics |
| Organization Unit | |
| Authors |
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| Presentation Type | paper |
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
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| Event Title | International Conference on Computational Social Science |
| Event Type | conference |
| Event Location | Helsinki, Finnland |
| Event Start Date | June 8 - 2015 |
| Event End Date | June 11 - 2015 |
| Abstract Text | Many systems exhibit patterns of interaction that are largely sparse and volatile at the same time. Sparsity is a common trait in networks where links are costly, or the nodes involved have some kind of capacity constraints; examples of this kind of behaviour are scientific and R&D collaborations and processes of opinion formation and disease spreading. In all these examples, agents do not maintain active the links to all their partners continuously. The sparsity of the network at any point in time determines the existence of a stable backbone whose evolution is slower than the typical time-scales of link creation and removal. The network backbone implies that between two consecutive observational time-windows, a footprint of the aggregated network will persist. However, in general in temporal networks, not all edges persist, and therefore there exists also a characteristic time after which a given instantaneous network becomes largely decorrelated with the previous one. Volatility then refers to the renewal process of edges in the stable backbone; the more fluid is said process, the more volatile the temporal network. When a dynamical process unravels on a temporal network, three ingredients have competing effects on the global properties of the system. First, the characteristic time-scales of the dynamical process itself; this has also influence on the convergence times for global phenomena to emerge. Second, the lifetime distribution of edges in the network, that determines the extent to which dynamical states can propagate. Third, the typical backbone of the network; a simple proxy for this quantity is the typical network density that is observed, instantaneously, in the system. In this Contribution, we study the role of network volatility in paradigmatic models of spreading developing on temporal networks, namely the Susceptible-Infected-Susceptible and Susceptible-Infected- Recovered-Susceptible ones. To do so, we introduce a minimalistic model of temporal networks such that different aspects of network volatility can be studied. In particular, we investigate under which conditions the relative sparseness and edge volatility of the temporal network can replace global connectivity in triggering collective behaviour. We show that collective phenomena can emerge purely induced by network dynamics when the network volatility is sufficiently large. We further demonstrate that long-range cor- relations can emerge in the system, even if the system remains largely disconnected while the temporal network evolves. Interestingly, we demonstrate a non-trivial relationship between the different aspects of network volatility, like the relative size of the stable backbone ρ, and the emergent properties of the processes taking place at the nodes of the system. First, it is clear that there exists a critical point for the size of the stable backbone such that the instantaneous network becomes disconnected (i.e. it experiences a percolation transition). Then, we show that also as a function of the size of the stable backbone, the dynamical processes analysed experience phase transitions towards different dynamical states (like coexistence, or synchronous behaviour) purely induced by the network dynamics. We show that such dynamic phase transition is not related to the percolation transition mentioned above, having both different critical points. Therefore, the phenomenon we show is not a simple results of the instantaneous network becoming dis- connected, but indeed an emergent property of network dynamics. |
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