189,915 research outputs found
Learning flexible representations of stochastic processes on graphs
Graph convolutional networks adapt the architecture of convolutional neural
networks to learn rich representations of data supported on arbitrary graphs by
replacing the convolution operations of convolutional neural networks with
graph-dependent linear operations. However, these graph-dependent linear
operations are developed for scalar functions supported on undirected graphs.
We propose a class of linear operations for stochastic (time-varying) processes
on directed (or undirected) graphs to be used in graph convolutional networks.
We propose a parameterization of such linear operations using functional
calculus to achieve arbitrarily low learning complexity. The proposed approach
is shown to model richer behaviors and display greater flexibility in learning
representations than product graph methods
STWalk: Learning Trajectory Representations in Temporal Graphs
Analyzing the temporal behavior of nodes in time-varying graphs is useful for
many applications such as targeted advertising, community evolution and outlier
detection. In this paper, we present a novel approach, STWalk, for learning
trajectory representations of nodes in temporal graphs. The proposed framework
makes use of structural properties of graphs at current and previous time-steps
to learn effective node trajectory representations. STWalk performs random
walks on a graph at a given time step (called space-walk) as well as on graphs
from past time-steps (called time-walk) to capture the spatio-temporal behavior
of nodes. We propose two variants of STWalk to learn trajectory
representations. In one algorithm, we perform space-walk and time-walk as part
of a single step. In the other variant, we perform space-walk and time-walk
separately and combine the learned representations to get the final trajectory
embedding. Extensive experiments on three real-world temporal graph datasets
validate the effectiveness of the learned representations when compared to
three baseline methods. We also show the goodness of the learned trajectory
embeddings for change point detection, as well as demonstrate that arithmetic
operations on these trajectory representations yield interesting and
interpretable results.Comment: 10 pages, 5 figures, 2 table
Distributed Dictionary Learning
The paper studies distributed Dictionary Learning (DL) problems where the
learning task is distributed over a multi-agent network with time-varying
(nonsymmetric) connectivity. This formulation is relevant, for instance, in
big-data scenarios where massive amounts of data are collected/stored in
different spatial locations and it is unfeasible to aggregate and/or process
all the data in a fusion center, due to resource limitations, communication
overhead or privacy considerations. We develop a general distributed
algorithmic framework for the (nonconvex) DL problem and establish its
asymptotic convergence. The new method hinges on Successive Convex
Approximation (SCA) techniques coupled with i) a gradient tracking mechanism
instrumental to locally estimate the missing global information; and ii) a
consensus step, as a mechanism to distribute the computations among the agents.
To the best of our knowledge, this is the first distributed algorithm with
provable convergence for the DL problem and, more in general, bi-convex
optimization problems over (time-varying) directed graphs
Nonasymptotic Convergence Rates for Cooperative Learning Over Time-Varying Directed Graphs
We study the problem of distributed hypothesis testing with a network of
agents where some agents repeatedly gain access to information about the
correct hypothesis. The group objective is to globally agree on a joint
hypothesis that best describes the observed data at all the nodes. We assume
that the agents can interact with their neighbors in an unknown sequence of
time-varying directed graphs. Following the pioneering work of Jadbabaie,
Molavi, Sandroni, and Tahbaz-Salehi, we propose local learning dynamics which
combine Bayesian updates at each node with a local aggregation rule of private
agent signals. We show that these learning dynamics drive all agents to the set
of hypotheses which best explain the data collected at all nodes as long as the
sequence of interconnection graphs is uniformly strongly connected. Our main
result establishes a non-asymptotic, explicit, geometric convergence rate for
the learning dynamic
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