11,766 research outputs found
Graph Signal Processing: Overview, Challenges and Applications
Research in Graph Signal Processing (GSP) aims to develop tools for
processing data defined on irregular graph domains. In this paper we first
provide an overview of core ideas in GSP and their connection to conventional
digital signal processing. We then summarize recent developments in developing
basic GSP tools, including methods for sampling, filtering or graph learning.
Next, we review progress in several application areas using GSP, including
processing and analysis of sensor network data, biological data, and
applications to image processing and machine learning. We finish by providing a
brief historical perspective to highlight how concepts recently developed in
GSP build on top of prior research in other areas.Comment: To appear, Proceedings of the IEE
Local-set-based Graph Signal Reconstruction
Signal processing on graph is attracting more and more attentions. For a
graph signal in the low-frequency subspace, the missing data associated with
unsampled vertices can be reconstructed through the sampled data by exploiting
the smoothness of the graph signal. In this paper, the concept of local set is
introduced and two local-set-based iterative methods are proposed to
reconstruct bandlimited graph signal from sampled data. In each iteration, one
of the proposed methods reweights the sampled residuals for different vertices,
while the other propagates the sampled residuals in their respective local
sets. These algorithms are built on frame theory and the concept of local sets,
based on which several frames and contraction operators are proposed. We then
prove that the reconstruction methods converge to the original signal under
certain conditions and demonstrate the new methods lead to a significantly
faster convergence compared with the baseline method. Furthermore, the
correspondence between graph signal sampling and time-domain irregular sampling
is analyzed comprehensively, which may be helpful to future works on graph
signals. Computer simulations are conducted. The experimental results
demonstrate the effectiveness of the reconstruction methods in various sampling
geometries, imprecise priori knowledge of cutoff frequency, and noisy
scenarios.Comment: 28 pages, 9 figures, 6 tables, journal manuscrip
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An Optimized Structure-Function Design Principle Underlies Efficient Signaling Dynamics in Neurons.
Dynamic signaling on branching axons is critical for rapid and efficient communication between neurons in the brain. Efficient signaling in axon arbors depends on a trade-off between the time it takes action potentials to reach synaptic terminals (temporal cost) and the amount of cellular material associated with the wiring path length of the neuron's morphology (material cost). However, where the balance between structural and dynamical considerations for achieving signaling efficiency is, and the design principle that neurons optimize to preserve this balance, is still elusive. In this work, we introduce a novel analysis that compares morphology and signaling dynamics in axonal networks to address this open problem. We show that in Basket cell neurons the design principle being optimized is the ratio between the refractory period of the membrane, and action potential latencies between the initial segment and the synaptic terminals. Our results suggest that the convoluted paths taken by axons reflect a design compensation by the neuron to slow down signaling latencies in order to optimize this ratio. Deviations in this ratio may result in a breakdown of signaling efficiency in the cell. These results pave the way to new approaches for investigating more complex neurophysiological phenomena that involve considerations of neuronal structure-function relationships
Sampling and Reconstruction of Sparse Signals on Circulant Graphs - An Introduction to Graph-FRI
With the objective of employing graphs toward a more generalized theory of
signal processing, we present a novel sampling framework for (wavelet-)sparse
signals defined on circulant graphs which extends basic properties of Finite
Rate of Innovation (FRI) theory to the graph domain, and can be applied to
arbitrary graphs via suitable approximation schemes. At its core, the
introduced Graph-FRI-framework states that any K-sparse signal on the vertices
of a circulant graph can be perfectly reconstructed from its
dimensionality-reduced representation in the graph spectral domain, the Graph
Fourier Transform (GFT), of minimum size 2K. By leveraging the recently
developed theory of e-splines and e-spline wavelets on graphs, one can
decompose this graph spectral transformation into the multiresolution low-pass
filtering operation with a graph e-spline filter, and subsequent transformation
to the spectral graph domain; this allows to infer a distinct sampling pattern,
and, ultimately, the structure of an associated coarsened graph, which
preserves essential properties of the original, including circularity and,
where applicable, the graph generating set.Comment: To appear in Appl. Comput. Harmon. Anal. (2017
DeepInf: Social Influence Prediction with Deep Learning
Social and information networking activities such as on Facebook, Twitter,
WeChat, and Weibo have become an indispensable part of our everyday life, where
we can easily access friends' behaviors and are in turn influenced by them.
Consequently, an effective social influence prediction for each user is
critical for a variety of applications such as online recommendation and
advertising.
Conventional social influence prediction approaches typically design various
hand-crafted rules to extract user- and network-specific features. However,
their effectiveness heavily relies on the knowledge of domain experts. As a
result, it is usually difficult to generalize them into different domains.
Inspired by the recent success of deep neural networks in a wide range of
computing applications, we design an end-to-end framework, DeepInf, to learn
users' latent feature representation for predicting social influence. In
general, DeepInf takes a user's local network as the input to a graph neural
network for learning her latent social representation. We design strategies to
incorporate both network structures and user-specific features into
convolutional neural and attention networks. Extensive experiments on Open
Academic Graph, Twitter, Weibo, and Digg, representing different types of
social and information networks, demonstrate that the proposed end-to-end
model, DeepInf, significantly outperforms traditional feature engineering-based
approaches, suggesting the effectiveness of representation learning for social
applications.Comment: 10 pages, 5 figures, to appear in KDD 2018 proceeding
A Distributed Tracking Algorithm for Reconstruction of Graph Signals
The rapid development of signal processing on graphs provides a new
perspective for processing large-scale data associated with irregular domains.
In many practical applications, it is necessary to handle massive data sets
through complex networks, in which most nodes have limited computing power.
Designing efficient distributed algorithms is critical for this task. This
paper focuses on the distributed reconstruction of a time-varying bandlimited
graph signal based on observations sampled at a subset of selected nodes. A
distributed least square reconstruction (DLSR) algorithm is proposed to recover
the unknown signal iteratively, by allowing neighboring nodes to communicate
with one another and make fast updates. DLSR uses a decay scheme to annihilate
the out-of-band energy occurring in the reconstruction process, which is
inevitably caused by the transmission delay in distributed systems. Proof of
convergence and error bounds for DLSR are provided in this paper, suggesting
that the algorithm is able to track time-varying graph signals and perfectly
reconstruct time-invariant signals. The DLSR algorithm is numerically
experimented with synthetic data and real-world sensor network data, which
verifies its ability in tracking slowly time-varying graph signals.Comment: 30 pages, 9 figures, 2 tables, journal pape
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