128,707 research outputs found
Constrained information flows in temporal networks reveal intermittent communities
Many real-world networks represent dynamic systems with interactions that
change over time, often in uncoordinated ways and at irregular intervals. For
example, university students connect in intermittent groups that repeatedly
form and dissolve based on multiple factors, including their lectures,
interests, and friends. Such dynamic systems can be represented as multilayer
networks where each layer represents a snapshot of the temporal network. In
this representation, it is crucial that the links between layers accurately
capture real dependencies between those layers. Often, however, these
dependencies are unknown. Therefore, current methods connect layers based on
simplistic assumptions that do not capture node-level layer dependencies. For
example, connecting every node to itself in other layers with the same weight
can wipe out dependencies between intermittent groups, making it difficult or
even impossible to identify them. In this paper, we present a principled
approach to estimating node-level layer dependencies based on the network
structure within each layer. We implement our node-level coupling method in the
community detection framework Infomap and demonstrate its performance compared
to current methods on synthetic and real temporal networks. We show that our
approach more effectively constrains information inside multilayer communities
so that Infomap can better recover planted groups in multilayer benchmark
networks that represent multiple modes with different groups and better
identify intermittent communities in real temporal contact networks. These
results suggest that node-level layer coupling can improve the modeling of
information spreading in temporal networks and better capture intermittent
community structure.Comment: 10 pages, 10 figures, published in PR
Tree Memory Networks for Modelling Long-term Temporal Dependencies
In the domain of sequence modelling, Recurrent Neural Networks (RNN) have
been capable of achieving impressive results in a variety of application areas
including visual question answering, part-of-speech tagging and machine
translation. However this success in modelling short term dependencies has not
successfully transitioned to application areas such as trajectory prediction,
which require capturing both short term and long term relationships. In this
paper, we propose a Tree Memory Network (TMN) for modelling long term and short
term relationships in sequence-to-sequence mapping problems. The proposed
network architecture is composed of an input module, controller and a memory
module. In contrast to related literature, which models the memory as a
sequence of historical states, we model the memory as a recursive tree
structure. This structure more effectively captures temporal dependencies
across both short term and long term sequences using its hierarchical
structure. We demonstrate the effectiveness and flexibility of the proposed TMN
in two practical problems, aircraft trajectory modelling and pedestrian
trajectory modelling in a surveillance setting, and in both cases we outperform
the current state-of-the-art. Furthermore, we perform an in depth analysis on
the evolution of the memory module content over time and provide visual
evidence on how the proposed TMN is able to map both long term and short term
relationships efficiently via a hierarchical structure
Quantile Correlations: Uncovering temporal dependencies in financial time series
We conduct an empirical study using the quantile-based correlation function
to uncover the temporal dependencies in financial time series. The study uses
intraday data for the S\&P 500 stocks from the New York Stock Exchange. After
establishing an empirical overview we compare the quantile-based correlation
function to stochastic processes from the GARCH family and find striking
differences. This motivates us to propose the quantile-based correlation
function as a powerful tool to assess the agreements between stochastic
processes and empirical data
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