8,460 research outputs found
Diffusion on networked systems is a question of time or structure
Network science investigates the architecture of complex systems to understand their functional and dynamical properties. Structural patterns such as communities shape diffusive processes on networks. However, these results hold under the strong assumption that networks are static entities where temporal aspects can be neglected. Here we propose a generalized formalism for linear dynamics on complex networks, able to incorporate statistical properties of the timings at which events occur. We show that the diffusion dynamics is affected by the network community structure and by the temporal properties of waiting times between events. We identify the main mechanism—network structure, burstiness or fat tails of waiting times—determining the relaxation times of stochastic processes on temporal networks, in the absence of temporal–structure correlations. We identify situations when fine-scale structure can be discarded from the description of the dynamics or, conversely, when a fully detailed model is required due to temporal heterogeneities
Structural network heterogeneities and network dynamics: a possible dynamical mechanism for hippocampal memory reactivation
The hippocampus has the capacity for reactivating recently acquired memories
[1-3] and it is hypothesized that one of the functions of sleep reactivation is
the facilitation of consolidation of novel memory traces [4-11]. The dynamic
and network processes underlying such a reactivation remain, however, unknown.
We show that such a reactivation characterized by local, self-sustained
activity of a network region may be an inherent property of the recurrent
excitatory-inhibitory network with a heterogeneous structure. The entry into
the reactivation phase is mediated through a physiologically feasible
regulation of global excitability and external input sources, while the
reactivated component of the network is formed through induced network
heterogeneities during learning. We show that structural changes needed for
robust reactivation of a given network region are well within known
physiological parameters [12,13].Comment: 16 pages, 5 figure
From calls to communities: a model for time varying social networks
Social interactions vary in time and appear to be driven by intrinsic
mechanisms, which in turn shape the emerging structure of the social network.
Large-scale empirical observations of social interaction structure have become
possible only recently, and modelling their dynamics is an actual challenge.
Here we propose a temporal network model which builds on the framework of
activity-driven time-varying networks with memory. The model also integrates
key mechanisms that drive the formation of social ties - social reinforcement,
focal closure and cyclic closure, which have been shown to give rise to
community structure and the global connectedness of the network. We compare the
proposed model with a real-world time-varying network of mobile phone
communication and show that they share several characteristics from
heterogeneous degrees and weights to rich community structure. Further, the
strong and weak ties that emerge from the model follow similar weight-topology
correlations as real-world social networks, including the role of weak ties.Comment: 10 pages, 5 figure
Exploring Temporal Networks with Greedy Walks
Temporal networks come with a wide variety of heterogeneities, from
burstiness of event sequences to correlations between timings of node and link
activations. In this paper, we set to explore the latter by using greedy walks
as probes of temporal network structure. Given a temporal network (a sequence
of contacts), greedy walks proceed from node to node by always following the
first available contact. Because of this, their structure is particularly
sensitive to temporal-topological patterns involving repeated contacts between
sets of nodes. This becomes evident in their small coverage per step as
compared to a temporal reference model -- in empirical temporal networks,
greedy walks often get stuck within small sets of nodes because of correlated
contact patterns. While this may also happen in static networks that have
pronounced community structure, the use of the temporal reference model takes
the underlying static network structure out of the equation and indicates that
there is a purely temporal reason for the observations. Further analysis of the
structure of greedy walks indicates that burst trains, sequences of repeated
contacts between node pairs, are the dominant factor. However, there are larger
patterns too, as shown with non-backtracking greedy walks. We proceed further
to study the entropy rates of greedy walks, and show that the sequences of
visited nodes are more structured and predictable in original data as compared
to temporally uncorrelated references. Taken together, these results indicate a
richness of correlated temporal-topological patterns in temporal networks
Activity clocks: spreading dynamics on temporal networks of human contact
Dynamical processes on time-varying complex networks are key to understanding
and modeling a broad variety of processes in socio-technical systems. Here we
focus on empirical temporal networks of human proximity and we aim at
understanding the factors that, in simulation, shape the arrival time
distribution of simple spreading processes. Abandoning the notion of wall-clock
time in favour of node-specific clocks based on activity exposes robust
statistical patterns in the arrival times across different social contexts.
Using randomization strategies and generative models constrained by data, we
show that these patterns can be understood in terms of heterogeneous
inter-event time distributions coupled with heterogeneous numbers of events per
edge. We also show, both empirically and by using a synthetic dataset, that
significant deviations from the above behavior can be caused by the presence of
edge classes with strong activity correlations
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