87,896 research outputs found
Competitive dynamics of lexical innovations in multi-layer networks
We study the introduction of lexical innovations into a community of language
users. Lexical innovations, i.e., new terms added to people's vocabulary, play
an important role in the process of language evolution. Nowadays, information
is spread through a variety of networks, including, among others, online and
offline social networks and the World Wide Web. The entire system, comprising
networks of different nature, can be represented as a multi-layer network. In
this context, lexical innovations diffusion occurs in a peculiar fashion. In
particular, a lexical innovation can undergo three different processes: its
original meaning is accepted; its meaning can be changed or misunderstood
(e.g., when not properly explained), hence more than one meaning can emerge in
the population; lastly, in the case of a loan word, it can be translated into
the population language (i.e., defining a new lexical innovation or using a
synonym) or into a dialect spoken by part of the population. Therefore, lexical
innovations cannot be considered simply as information. We develop a model for
analyzing this scenario using a multi-layer network comprising a social network
and a media network. The latter represents the set of all information systems
of a society, e.g., television, the World Wide Web and radio. Furthermore, we
identify temporal directed edges between the nodes of these two networks. In
particular, at each time step, nodes of the media network can be connected to
randomly chosen nodes of the social network and vice versa. In so doing,
information spreads through the whole system and people can share a lexical
innovation with their neighbors or, in the event they work as reporters, by
using media nodes. Lastly, we use the concept of "linguistic sign" to model
lexical innovations, showing its fundamental role in the study of these
dynamics. Many numerical simulations have been performed.Comment: 23 pages, 19 figures, 1 tabl
Diffusion and Contagion in Networks with Heterogeneous Agents and Homophily
We study how a behavior (an idea, buying a product, having a disease,
adopting a cultural fad or a technology) spreads among agents in an a social
network that exhibits segregation or homophily (the tendency of agents to
associate with others similar to themselves). Individuals are distinguished by
their types (e.g., race, gender, age, wealth, religion, profession, etc.)
which, together with biased interaction patterns, induce heterogeneous rates of
adoption. We identify the conditions under which a behavior diffuses and
becomes persistent in the population. These conditions relate to the level of
homophily in a society, the underlying proclivities of various types for
adoption or infection, as well as how each type interacts with its own type. In
particular, we show that homophily can facilitate diffusion from a small
initial seed of adopters.Comment: 18 pages, 1 figur
Traveling and pinned fronts in bistable reaction-diffusion systems on network
Traveling fronts and stationary localized patterns in bistable
reaction-diffusion systems have been broadly studied for classical continuous
media and regular lattices. Analogs of such non-equilibrium patterns are also
possible in networks. Here, we consider traveling and stationary patterns in
bistable one-component systems on random Erd\"os-R\'enyi, scale-free and
hierarchical tree networks. As revealed through numerical simulations,
traveling fronts exist in network-organized systems. They represent waves of
transition from one stable state into another, spreading over the entire
network. The fronts can furthermore be pinned, thus forming stationary
structures. While pinning of fronts has previously been considered for chains
of diffusively coupled bistable elements, the network architecture brings about
significant differences. An important role is played by the degree (the number
of connections) of a node. For regular trees with a fixed branching factor, the
pinning conditions are analytically determined. For large Erd\"os-R\'enyi and
scale-free networks, the mean-field theory for stationary patterns is
constructed
Classes of random walks on temporal networks with competing timescales
Random walks find applications in many areas of science and are the heart of
essential network analytic tools. When defined on temporal networks, even basic
random walk models may exhibit a rich spectrum of behaviours, due to the
co-existence of different timescales in the system. Here, we introduce random
walks on general stochastic temporal networks allowing for lasting
interactions, with up to three competing timescales. We then compare the mean
resting time and stationary state of different models. We also discuss the
accuracy of the mathematical analysis depending on the random walk model and
the structure of the underlying network, and pay particular attention to the
emergence of non-Markovian behaviour, even when all dynamical entities are
governed by memoryless distributions.Comment: 16 pages, 5 figure
Active mechanics reveal molecular-scale force kinetics in living oocytes
Active diffusion of intracellular components is emerging as an important
process in cell biology. This process is mediated by complex assemblies of
molecular motors and cytoskeletal filaments that drive force generation in the
cytoplasm and facilitate enhanced motion. The kinetics of molecular motors have
been precisely characterized in-vitro by single molecule approaches, however,
their in-vivo behavior remains elusive. Here, we study the active diffusion of
vesicles in mouse oocytes, where this process plays a key role in nuclear
positioning during development, and combine an experimental and theoretical
framework to extract molecular-scale force kinetics (force, power-stroke, and
velocity) of the in-vivo active process. Assuming a single dominant process, we
find that the nonequilibrium activity induces rapid kicks of duration 300 s resulting in an average force of 0.4 pN on vesicles
in in-vivo oocytes, remarkably similar to the kinetics of in-vitro myosin-V.
Our results reveal that measuring in-vivo active fluctuations allows extraction
of the molecular-scale activity in agreement with single-molecule studies and
demonstrates a mesoscopic framework to access force kinetics.Comment: 20 pages, 4 figures, see ancillary files for Supplementary Materials,
* equally contributing author
Structure formation in active networks
Structure formation and constant reorganization of the actin cytoskeleton are
key requirements for the function of living cells. Here we show that a minimal
reconstituted system consisting of actin filaments, crosslinking molecules and
molecular-motor filaments exhibits a generic mechanism of structure formation,
characterized by a broad distribution of cluster sizes. We demonstrate that the
growth of the structures depends on the intricate balance between
crosslinker-induced stabilization and simultaneous destabilization by molecular
motors, a mechanism analogous to nucleation and growth in passive systems. We
also show that the intricate interplay between force generation, coarsening and
connectivity is responsible for the highly dynamic process of structure
formation in this heterogeneous active gel, and that these competing mechanisms
result in anomalous transport, reminiscent of intracellular dynamics
Modelling cytoskeletal traffic: an interplay between passive diffusion and active transport
We introduce the totally asymmetric exclusion process with Langmuir kinetics
(TASEP-LK) on a network as a microscopic model for active motor protein
transport on the cytoskeleton, immersed in the diffusive cytoplasm. We discuss
how the interplay between active transport along a network and infinite
diffusion in a bulk reservoir leads to a heterogeneous matter distribution on
various scales. We find three regimes for steady state transport, corresponding
to the scale of the network, of individual segments or local to sites. At low
exchange rates strong density heterogeneities develop between different
segments in the network. In this regime one has to consider the topological
complexity of the whole network to describe transport. In contrast, at moderate
exchange rates the transport through the network decouples, and the physics is
determined by single segments and the local topology. At last, for very high
exchange rates the homogeneous Langmuir process dominates the stationary state.
We introduce effective rate diagrams for the network to identify these
different regimes. Based on this method we develop an intuitive but generic
picture of how the stationary state of excluded volume processes on complex
networks can be understood in terms of the single-segment phase diagram.Comment: 5 pages, 7 figure
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