2,275 research outputs found
Efficient local strategies for vaccination and network attack
We study how a fraction of a population should be vaccinated to most
efficiently top epidemics. We argue that only local information (about the
neighborhood of specific vertices) is usable in practice, and hence we consider
only local vaccination strategies. The efficiency of the vaccination strategies
is investigated with both static and dynamical measures. Among other things we
find that the most efficient strategy for many real-world situations is to
iteratively vaccinate the neighbor of the previous vaccinee that has most links
out of the neighborhood
Cascade control and defense in complex networks
Complex networks with heterogeneous distribution of loads may undergo a
global cascade of overload failures when highly loaded nodes or edges are
removed due to attacks or failures. Since a small attack or failure has the
potential to trigger a global cascade, a fundamental question regards the
possible strategies of defense to prevent the cascade from propagating through
the entire network. Here we introduce and investigate a costless strategy of
defense based on a selective further removal of nodes and edges, right after
the initial attack or failure. This intentional removal of network elements is
shown to drastically reduce the size of the cascade.Comment: 4 pages, 2 figures, Revte
Network dynamics of ongoing social relationships
Many recent large-scale studies of interaction networks have focused on
networks of accumulated contacts. In this paper we explore social networks of
ongoing relationships with an emphasis on dynamical aspects. We find a
distribution of response times (times between consecutive contacts of different
direction between two actors) that has a power-law shape over a large range. We
also argue that the distribution of relationship duration (the time between the
first and last contacts between actors) is exponentially decaying. Methods to
reanalyze the data to compensate for the finite sampling time are proposed. We
find that the degree distribution for networks of ongoing contacts fits better
to a power-law than the degree distribution of the network of accumulated
contacts do. We see that the clustering and assortative mixing coefficients are
of the same order for networks of ongoing and accumulated contacts, and that
the structural fluctuations of the former are rather large.Comment: to appear in Europhys. Let
A network-based threshold model for the spreading of fads in society and markets
We investigate the behavior of a threshold model for the spreading of fads
and similar phenomena in society. The model is giving the fad dynamics and is
intended to be confined to an underlying network structure. We investigate the
whole parameter space of the fad dynamics on three types of network models. The
dynamics we discover is rich and highly dependent on the underlying network
structure. For some range of the parameter space, for all types of substrate
networks, there are a great variety of sizes and life-lengths of the fads --
what one see in real-world social and economical systems
Tax evasion dynamics and Zaklan model on Opinion-dependent Network
Within the context of agent-based Monte-Carlo simulations, we study the
well-known majority-vote model (MVM) with noise applied to tax evasion on
Stauffer-Hohnisch-Pittnauer (SHP) networks. To control the fluctuations for tax
evasion in the economics model proposed by Zaklan, MVM is applied in the
neighborhood of the critical noise to evolve the Zaklan model. The
Zaklan model had been studied recently using the equilibrium Ising model. Here
we show that the Zaklan model is robust because this can be studied besides
using equilibrium dynamics of Ising model also through the nonequilibrium MVM
and on various topologies giving the same behavior regardless of dynamic or
topology used here.Comment: 14 page, 4 figure
The diplomat's dilemma: Maximal power for minimal effort in social networks
Closeness is a global measure of centrality in networks, and a proxy for how
influential actors are in social networks. In most network models, and many
empirical networks, closeness is strongly correlated with degree. However, in
social networks there is a cost of maintaining social ties. This leads to a
situation (that can occur in the professional social networks of executives,
lobbyists, diplomats and so on) where agents have the conflicting objectives of
aiming for centrality while simultaneously keeping the degree low. We
investigate this situation in an adaptive network-evolution model where agents
optimize their positions in the network following individual strategies, and
using only local information. The strategies are also optimized, based on the
success of the agent and its neighbors. We measure and describe the time
evolution of the network and the agents' strategies.Comment: Submitted to Adaptive Networks: Theory, Models and Applications, to
be published from Springe
Role of Activity in Human Dynamics
The human society is a very complex system; still, there are several
non-trivial, general features. One type of them is the presence of power-law
distributed quantities in temporal statistics. In this Letter, we focus on the
origin of power-laws in rating of movies. We present a systematic empirical
exploration of the time between two consecutive ratings of movies (the
interevent time). At an aggregate level, we find a monotonous relation between
the activity of individuals and the power-law exponent of the interevent-time
distribution. At an individual level, we observe a heavy-tailed distribution
for each user, as well as a negative correlation between the activity and the
width of the distribution. We support these findings by a similar data set from
mobile phone text-message communication. Our results demonstrate a significant
role of the activity of individuals on the society-level patterns of human
behavior. We believe this is a common character in the interest-driven human
dynamics, corresponding to (but different from) the universality classes of
task-driven dynamics.Comment: 5 pages, 6 figures. Accepted by EP
Handling oversampling in dynamic networks using link prediction
Oversampling is a common characteristic of data representing dynamic
networks. It introduces noise into representations of dynamic networks, but
there has been little work so far to compensate for it. Oversampling can affect
the quality of many important algorithmic problems on dynamic networks,
including link prediction. Link prediction seeks to predict edges that will be
added to the network given previous snapshots. We show that not only does
oversampling affect the quality of link prediction, but that we can use link
prediction to recover from the effects of oversampling. We also introduce a
novel generative model of noise in dynamic networks that represents
oversampling. We demonstrate the results of our approach on both synthetic and
real-world data.Comment: ECML/PKDD 201
Signatures of currency vertices
Many real-world networks have broad degree distributions. For some systems,
this means that the functional significance of the vertices is also broadly
distributed, in other cases the vertices are equally significant, but in
different ways. One example of the latter case is metabolic networks, where the
high-degree vertices -- the currency metabolites -- supply the molecular groups
to the low-degree metabolites, and the latter are responsible for the
higher-order biological function, of vital importance to the organism. In this
paper, we propose a generalization of currency metabolites to currency
vertices. We investigate the network structural characteristics of such
systems, both in model networks and in some empirical systems. In addition to
metabolic networks, we find that a network of music collaborations and a
network of e-mail exchange could be described by a division of the vertices
into currency vertices and others.Comment: to appear in Journal of the Physical Society of Japa
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