7,033 research outputs found
Random walks on temporal networks
Many natural and artificial networks evolve in time. Nodes and connections
appear and disappear at various timescales, and their dynamics has profound
consequences for any processes in which they are involved. The first empirical
analysis of the temporal patterns characterizing dynamic networks are still
recent, so that many questions remain open. Here, we study how random walks, as
paradigm of dynamical processes, unfold on temporally evolving networks. To
this aim, we use empirical dynamical networks of contacts between individuals,
and characterize the fundamental quantities that impact any general process
taking place upon them. Furthermore, we introduce different randomizing
strategies that allow us to single out the role of the different properties of
the empirical networks. We show that the random walk exploration is slower on
temporal networks than it is on the aggregate projected network, even when the
time is properly rescaled. In particular, we point out that a fundamental role
is played by the temporal correlations between consecutive contacts present in
the data. Finally, we address the consequences of the intrinsically limited
duration of many real world dynamical networks. Considering the fundamental
prototypical role of the random walk process, we believe that these results
could help to shed light on the behavior of more complex dynamics on temporally
evolving networks.Comment: 14 pages, 13 figure
Causal Discovery in Physical Systems from Videos
Causal discovery is at the core of human cognition. It enables us to reason about the environment and make counterfactual predictions about unseen scenarios that can vastly differ from our previous experiences. We consider the task of causal discovery from videos in an end-to-end fashion without supervision on the ground-truth graph structure. In particular, our goal is to discover the structural dependencies among environmental and object variables: inferring the type and strength of interactions that have a causal effect on the behavior of the dynamical system. Our model consists of (a) a perception module that extracts a semantically meaningful and temporally consistent keypoint representation from images, (b) an inference module for determining the graph distribution induced by the detected keypoints, and (c) a dynamics module that can predict the future by conditioning on the inferred graph. We assume access to different configurations and environmental conditions, i.e., data from unknown interventions on the underlying system; thus, we can hope to discover the correct underlying causal graph without explicit interventions. We evaluate our method in a planar multi-body interaction environment and scenarios involving fabrics of different shapes like shirts and pants. Experiments demonstrate that our model can correctly identify the interactions from a short sequence of images and make long-term future predictions. The causal structure assumed by the model also allows it to make counterfactual predictions and extrapolate to systems of unseen interaction graphs or graphs of various sizes
Causal Discovery in Physical Systems from Videos
Causal discovery is at the core of human cognition. It enables us to reason
about the environment and make counterfactual predictions about unseen
scenarios that can vastly differ from our previous experiences. We consider the
task of causal discovery from videos in an end-to-end fashion without
supervision on the ground-truth graph structure. In particular, our goal is to
discover the structural dependencies among environmental and object variables:
inferring the type and strength of interactions that have a causal effect on
the behavior of the dynamical system. Our model consists of (a) a perception
module that extracts a semantically meaningful and temporally consistent
keypoint representation from images, (b) an inference module for determining
the graph distribution induced by the detected keypoints, and (c) a dynamics
module that can predict the future by conditioning on the inferred graph. We
assume access to different configurations and environmental conditions, i.e.,
data from unknown interventions on the underlying system; thus, we can hope to
discover the correct underlying causal graph without explicit interventions. We
evaluate our method in a planar multi-body interaction environment and
scenarios involving fabrics of different shapes like shirts and pants.
Experiments demonstrate that our model can correctly identify the interactions
from a short sequence of images and make long-term future predictions. The
causal structure assumed by the model also allows it to make counterfactual
predictions and extrapolate to systems of unseen interaction graphs or graphs
of various sizes.Comment: NeurIPS 2020. Project page: https://yunzhuli.github.io/V-CDN
Does Systematic Sampling Preserve Granger Causality with an Application to High Frequency Financial Data?
In applied econometric literature, the causal inferences are often made based on temporally aggregated or systematically sampled data. A number of studies document that temporal aggregation has distorting effects on causal inference and systematic sampling of stationary variables preserves the direction of causality. Contrary to the stationary case, this paper shows for the bivariate VAR(1) system that systematic sampling induces spurious bi-directional Granger causality among the variables if the uni-directional causality runs from a non-stationary series to either a stationary or a non-stationary series. An empirical exercise illustrates the relative usefulness of the results further
Non-Parametric Causality Detection: An Application to Social Media and Financial Data
According to behavioral finance, stock market returns are influenced by
emotional, social and psychological factors. Several recent works support this
theory by providing evidence of correlation between stock market prices and
collective sentiment indexes measured using social media data. However, a pure
correlation analysis is not sufficient to prove that stock market returns are
influenced by such emotional factors since both stock market prices and
collective sentiment may be driven by a third unmeasured factor. Controlling
for factors that could influence the study by applying multivariate regression
models is challenging given the complexity of stock market data. False
assumptions about the linearity or non-linearity of the model and inaccuracies
on model specification may result in misleading conclusions.
In this work, we propose a novel framework for causal inference that does not
require any assumption about the statistical relationships among the variables
of the study and can effectively control a large number of factors. We apply
our method in order to estimate the causal impact that information posted in
social media may have on stock market returns of four big companies. Our
results indicate that social media data not only correlate with stock market
returns but also influence them.Comment: Physica A: Statistical Mechanics and its Applications 201
Causal discovery in a complex industrial system: A time series benchmark
Causal discovery outputs a causal structure, represented by a graph, from
observed data. For time series data, there is a variety of methods, however, it
is difficult to evaluate these on real data as realistic use cases very rarely
come with a known causal graph to which output can be compared. In this paper,
we present a dataset from an industrial subsystem at the European Spallation
Source along with its causal graph which has been constructed from expert
knowledge. This provides a testbed for causal discovery from time series
observations of complex systems, and we believe this can help inform the
development of causal discovery methodology.Comment: 18 pages, 9 figures, 1 tabl
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The Growth and Decline of the Western Roman Empire: Quantifying the Dynamics of Army Size, Territory, and Coinage
We model the Western Roman Empire from 500 BCE to 500 CE, aiming to understand the interdependent dynamics of army size, conquered territory and the production and debasement of coins within the empire. The relationships are represented through feedback relationships and modelled mathematically via a dynamical system, specified as a set of ordinary differential equations. We analyze the stability of a subsystem and determine that it is neutrally stable. Based on this, we find that to prevent decline, the optimal policy was to stop debasement and reduce the army size and territory during the rule of Marcus Aurelius. Given the nature of the stability of the system and the kind of policies necessary to prevent decline, we argue that a high degree of centralized control was necessary, in line with basic tenets of structural-demographic theory. This article was updated on 01/09/2020 to correct an error in equation (3.5). Page numbers were updated on 01/05/2021
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