50,234 research outputs found
Timing interactions in social simulations: The voter model
The recent availability of huge high resolution datasets on human activities
has revealed the heavy-tailed nature of the interevent time distributions. In
social simulations of interacting agents the standard approach has been to use
Poisson processes to update the state of the agents, which gives rise to very
homogeneous activity patterns with a well defined characteristic interevent
time. As a paradigmatic opinion model we investigate the voter model and review
the standard update rules and propose two new update rules which are able to
account for heterogeneous activity patterns. For the new update rules each node
gets updated with a probability that depends on the time since the last event
of the node, where an event can be an update attempt (exogenous update) or a
change of state (endogenous update). We find that both update rules can give
rise to power law interevent time distributions, although the endogenous one
more robustly. Apart from that for the exogenous update rule and the standard
update rules the voter model does not reach consensus in the infinite size
limit, while for the endogenous update there exist a coarsening process that
drives the system toward consensus configurations.Comment: Book Chapter, 23 pages, 9 figures, 5 table
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
We study the online influence maximization problem in social networks under
the independent cascade model. Specifically, we aim to learn the set of "best
influencers" in a social network online while repeatedly interacting with it.
We address the challenges of (i) combinatorial action space, since the number
of feasible influencer sets grows exponentially with the maximum number of
influencers, and (ii) limited feedback, since only the influenced portion of
the network is observed. Under a stochastic semi-bandit feedback, we propose
and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our
bounds on the cumulative regret are polynomial in all quantities of interest,
achieve near-optimal dependence on the number of interactions and reflect the
topology of the network and the activation probabilities of its edges, thereby
giving insights on the problem complexity. To the best of our knowledge, these
are the first such results. Our experiments show that in several representative
graph topologies, the regret of IMLinUCB scales as suggested by our upper
bounds. IMLinUCB permits linear generalization and thus is both statistically
and computationally suitable for large-scale problems. Our experiments also
show that IMLinUCB with linear generalization can lead to low regret in
real-world online influence maximization.Comment: Compared with the previous version, this version has fixed a mistake.
This version is also consistent with the NIPS camera-ready versio
Update rules and interevent time distributions: Slow ordering vs. no ordering in the Voter Model
We introduce a general methodology of update rules accounting for arbitrary
interevent time distributions in simulations of interacting agents. In
particular we consider update rules that depend on the state of the agent, so
that the update becomes part of the dynamical model. As an illustration we
consider the voter model in fully-connected, random and scale free networks
with an update probability inversely proportional to the persistence, that is,
the time since the last event. We find that in the thermodynamic limit, at
variance with standard updates, the system orders slowly. The approach to the
absorbing state is characterized by a power law decay of the density of
interfaces, observing that the mean time to reach the absorbing state might be
not well defined.Comment: 5pages, 4 figure
Model reproduces individual, group and collective dynamics of human contact networks
Empirical data on the dynamics of human face-to-face interactions across a variety of social venues have recently revealed a number of context-independent structural and temporal properties of human contact networks. This universality suggests that some basic mechanisms may be responsible for the unfolding of human interactions in the physical space. Here we discuss a simple model that reproduces the empirical distributions for the individual, group and collective dynamics of face-to-face contact networks. The model describes agents that move randomly in a two-dimensional space and tend to stop when meeting ‘attractive’ peers, and reproduces accurately the empirical distributions.Postprint (author's final draft
Modeling self-sustained activity cascades in socio-technical networks
The ability to understand and eventually predict the emergence of information
and activation cascades in social networks is core to complex socio-technical
systems research. However, the complexity of social interactions makes this a
challenging enterprise. Previous works on cascade models assume that the
emergence of this collective phenomenon is related to the activity observed in
the local neighborhood of individuals, but do not consider what determines the
willingness to spread information in a time-varying process. Here we present a
mechanistic model that accounts for the temporal evolution of the individual
state in a simplified setup. We model the activity of the individuals as a
complex network of interacting integrate-and-fire oscillators. The model
reproduces the statistical characteristics of the cascades in real systems, and
provides a framework to study time-evolution of cascades in a state-dependent
activity scenario.Comment: 5 pages, 3 figure
Joint effect of ageing and multilayer structure prevents ordering in the voter model
The voter model rules are simple, with agents copying the state of a random
neighbor, but they lead to non-trivial dynamics. Besides opinion processes, the
model has also applications for catalysis and species competition. Inspired by
the temporal inhomogeneities found in human interactions, one can introduce
ageing in the agents: the probability to update decreases with the time elapsed
since the last change. This modified dynamics induces an approach to consensus
via coarsening in complex networks. Additionally, multilayer networks produce
profound changes in the dynamics of models. In this work, we investigate how a
multilayer structure affects the dynamics of an ageing voter model. The system
is studied as a function of the fraction of nodes sharing states across layers
(multiplexity parameter q ). We find that the dynamics of the system suffers a
notable change at an intermediate value q*. Above it, the voter model always
orders to an absorbing configuration. While, below, a fraction of the
realizations falls into dynamical traps associated to a spontaneous symmetry
breaking in which the majority opinion in the different layers takes opposite
signs and that due to the ageing indefinitely delay the arrival at the
absorbing state.Comment: 10 pages, 8 figure
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