140 research outputs found
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
Effects of temporal correlations on cascades: Threshold models on temporal networks
A person's decision to adopt an idea or product is often driven by the
decisions of peers, mediated through a network of social ties. A common way of
modeling adoption dynamics is to use threshold models, where a node may become
an adopter given a high enough rate of contacts with adopted neighbors. We
study the dynamics of threshold models that take both the network topology and
the timings of contacts into account, using empirical contact sequences as
substrates. The models are designed such that adoption is driven by the number
of contacts with different adopted neighbors within a chosen time. We find that
while some networks support cascades leading to network-level adoption, some do
not: the propagation of adoption depends on several factors from the frequency
of contacts to burstiness and timing correlations of contact sequences. More
specifically, burstiness is seen to suppress cascades sizes when compared to
randomised contact timings, while timing correlations between contacts on
adjacent links facilitate cascades.Comment: 9 pages, 7 figures, Published versio
Ranking influential spreaders is an ill-defined problem
Finding influential spreaders of information and disease in networks is an
important theoretical problem, and one of considerable recent interest. It has
been almost exclusively formulated as a node-ranking problem -- methods for
identifying influential spreaders rank nodes according to how influential they
are. In this work, we show that the ranking approach does not necessarily work:
the set of most influential nodes depends on the number of nodes in the set.
Therefore, the set of most important nodes to vaccinate does not need to
have any node in common with the set of most important nodes. We propose
a method for quantifying the extent and impact of this phenomenon, and show
that it is common in both empirical and model networks
Two betweenness centrality measures based on Randomized Shortest Paths
This paper introduces two new closely related betweenness centrality measures
based on the Randomized Shortest Paths (RSP) framework, which fill a gap
between traditional network centrality measures based on shortest paths and
more recent methods considering random walks or current flows. The framework
defines Boltzmann probability distributions over paths of the network which
focus on the shortest paths, but also take into account longer paths depending
on an inverse temperature parameter. RSP's have previously proven to be useful
in defining distance measures on networks. In this work we study their utility
in quantifying the importance of the nodes of a network. The proposed RSP
betweenness centralities combine, in an optimal way, the ideas of using the
shortest and purely random paths for analysing the roles of network nodes,
avoiding issues involving these two paradigms. We present the derivations of
these measures and how they can be computed in an efficient way. In addition,
we show with real world examples the potential of the RSP betweenness
centralities in identifying interesting nodes of a network that more
traditional methods might fail to notice.Comment: Minor updates; published in Scientific Report
The strength of strong ties in scientific collaboration networks
Network topology and its relationship to tie strengths may hinder or enhance
the spreading of information in social networks. We study the correlations
between tie strengths and topology in networks of scientific collaboration, and
show that these are very different from ordinary social networks. For the
latter, it has earlier been shown that strong ties are associated with dense
network neighborhoods, while weaker ties act as bridges between these. Because
of this, weak links act as bottlenecks for the diffusion of information. We
show that on the contrary, in co-authorship networks dense local neighborhoods
mainly consist of weak links, whereas strong links are more important for
overall connectivity. The important role of strong links is further highlighted
in simulations of information spreading, where their topological position is
seen to dramatically speed up spreading dynamics. Thus, in contrast to ordinary
social networks, weight-topology correlations enhance the flow of information
across scientific collaboration networks.Comment: 6 Pages, 6 Figures, Published version, Minor changes, Results also
verified using new weight-schem
Temporal network sparsity and the slowing down of spreading
Interactions in time-varying complex systems are often very heterogeneous at
the topological level (who interacts with whom) and at the temporal level (when
interactions occur and how often). While it is known that temporal
heterogeneities often have strong effects on dynamical processes, e.g. the
burstiness of contact sequences is associated with slower spreading dynamics,
the picture is far from complete. In this paper, we show that temporal
heterogeneities result in temporal sparsity} at the time scale of average
inter-event times, and that temporal sparsity determines the amount of slowdown
of Susceptible-Infectious (SI) spreading dynamics on temporal networks. This
result is based on the analysis of several empirical temporal network data
sets. An approximate solution for a simple network model confirms the
association between temporal sparsity and slowdown of SI spreading dynamics.
Since deterministic SI spreading always follows the fastest temporal paths, our
results generalize -- paths are slower to traverse because of temporal
sparsity, and therefore all dynamical processes are slower as well
Critical drift in a neuro-inspired adaptive network
It has been postulated that the brain operates in a self-organized critical
state that brings multiple benefits, such as optimal sensitivity to input. Thus
far, self-organized criticality has typically been depicted as a
one-dimensional process, where one parameter is tuned to a critical value.
However, the number of adjustable parameters in the brain is vast, and hence
critical states can be expected to occupy a high-dimensional manifold inside a
high-dimensional parameter space. Here, we show that adaptation rules inspired
by homeostatic plasticity drive a neuro-inspired network to drift on a critical
manifold, where the system is poised between inactivity and persistent
activity. During the drift, global network parameters continue to change while
the system remains at criticality.Comment: 5 pages, 2 figure
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