3,827 research outputs found
Bias-Variance Tradeoff of Graph Laplacian Regularizer
This paper presents a bias-variance tradeoff of graph Laplacian regularizer,
which is widely used in graph signal processing and semi-supervised learning
tasks. The scaling law of the optimal regularization parameter is specified in
terms of the spectral graph properties and a novel signal-to-noise ratio
parameter, which suggests selecting a mediocre regularization parameter is
often suboptimal. The analysis is applied to three applications, including
random, band-limited, and multiple-sampled graph signals. Experiments on
synthetic and real-world graphs demonstrate near-optimal performance of the
established analysis.Comment: accepted by IEEE Signal Processing Letter
Revisiting Spectral Graph Clustering with Generative Community Models
The methodology of community detection can be divided into two principles:
imposing a network model on a given graph, or optimizing a designed objective
function. The former provides guarantees on theoretical detectability but falls
short when the graph is inconsistent with the underlying model. The latter is
model-free but fails to provide quality assurance for the detected communities.
In this paper, we propose a novel unified framework to combine the advantages
of these two principles. The presented method, SGC-GEN, not only considers the
detection error caused by the corresponding model mismatch to a given graph,
but also yields a theoretical guarantee on community detectability by analyzing
Spectral Graph Clustering (SGC) under GENerative community models (GCMs).
SGC-GEN incorporates the predictability on correct community detection with a
measure of community fitness to GCMs. It resembles the formulation of
supervised learning problems by enabling various community detection loss
functions and model mismatch metrics. We further establish a theoretical
condition for correct community detection using the normalized graph Laplacian
matrix under a GCM, which provides a novel data-driven loss function for
SGC-GEN. In addition, we present an effective algorithm to implement SGC-GEN,
and show that the computational complexity of SGC-GEN is comparable to the
baseline methods. Our experiments on 18 real-world datasets demonstrate that
SGC-GEN possesses superior and robust performance compared to 6 baseline
methods under 7 representative clustering metrics.Comment: Accepted by IEEE International Conference on Data Mining (ICDM) 2017
as a regular paper - full paper with supplementary materia
Attacking the Madry Defense Model with -based Adversarial Examples
The Madry Lab recently hosted a competition designed to test the robustness
of their adversarially trained MNIST model. Attacks were constrained to perturb
each pixel of the input image by a scaled maximal distortion
= 0.3. This discourages the use of attacks which are not optimized
on the distortion metric. Our experimental results demonstrate that
by relaxing the constraint of the competition, the elastic-net
attack to deep neural networks (EAD) can generate transferable adversarial
examples which, despite their high average distortion, have minimal
visual distortion. These results call into question the use of as a
sole measure for visual distortion, and further demonstrate the power of EAD at
generating robust adversarial examples.Comment: Accepted to ICLR 2018 Workshop
Multilayer Spectral Graph Clustering via Convex Layer Aggregation: Theory and Algorithms
Multilayer graphs are commonly used for representing different relations
between entities and handling heterogeneous data processing tasks. Non-standard
multilayer graph clustering methods are needed for assigning clusters to a
common multilayer node set and for combining information from each layer. This
paper presents a multilayer spectral graph clustering (SGC) framework that
performs convex layer aggregation. Under a multilayer signal plus noise model,
we provide a phase transition analysis of clustering reliability. Moreover, we
use the phase transition criterion to propose a multilayer iterative model
order selection algorithm (MIMOSA) for multilayer SGC, which features automated
cluster assignment and layer weight adaptation, and provides statistical
clustering reliability guarantees. Numerical simulations on synthetic
multilayer graphs verify the phase transition analysis, and experiments on
real-world multilayer graphs show that MIMOSA is competitive or better than
other clustering methods.Comment: Published at IEEE Transactions on Signal and Information Processing
over Network
Deep Community Detection
A deep community in a graph is a connected component that can only be seen
after removal of nodes or edges from the rest of the graph. This paper
formulates the problem of detecting deep communities as multi-stage node
removal that maximizes a new centrality measure, called the local Fiedler
vector centrality (LFVC), at each stage. The LFVC is associated with the
sensitivity of algebraic connectivity to node or edge removals. We prove that a
greedy node/edge removal strategy, based on successive maximization of LFVC,
has bounded performance loss relative to the optimal, but intractable,
combinatorial batch removal strategy. Under a stochastic block model framework,
we show that the greedy LFVC strategy can extract deep communities with
probability one as the number of observations becomes large. We apply the
greedy LFVC strategy to real-world social network datasets. Compared with
conventional community detection methods we demonstrate improved ability to
identify important communities and key members in the network.Comment: 15 pages, 13 figures, journal submission and supplementary file
(Figures 11-13), to appear in IEEE Transactions on Signal Processin
Sequential Defense Against Random and Intentional Attacks in Complex Networks
Network robustness against attacks is one of the most fundamental researches
in network science as it is closely associated with the reliability and
functionality of various networking paradigms. However, despite the study on
intrinsic topological vulnerabilities to node removals, little is known on the
network robustness when network defense mechanisms are implemented, especially
for networked engineering systems equipped with detection capabilities. In this
paper, a sequential defense mechanism is firstly proposed in complex networks
for attack inference and vulnerability assessment, where the data fusion center
sequentially infers the presence of an attack based on the binary attack status
reported from the nodes in the network. The network robustness is evaluated in
terms of the ability to identify the attack prior to network disruption under
two major attack schemes, i.e., random and intentional attacks. We provide a
parametric plug-in model for performance evaluation on the proposed mechanism
and validate its effectiveness and reliability via canonical complex network
models and real-world large-scale network topology. The results show that the
sequential defense mechanism greatly improves the network robustness and
mitigates the possibility of network disruption by acquiring limited attack
status information from a small subset of nodes in the network.Comment: 13 pages, 14 figure
Phase Transitions and a Model Order Selection Criterion for Spectral Graph Clustering
One of the longstanding open problems in spectral graph clustering (SGC) is
the so-called model order selection problem: automated selection of the correct
number of clusters. This is equivalent to the problem of finding the number of
connected components or communities in an undirected graph. We propose
automated model order selection (AMOS), a solution to the SGC model selection
problem under a random interconnection model (RIM) using a novel selection
criterion that is based on an asymptotic phase transition analysis. AMOS can
more generally be applied to discovering hidden block diagonal structure in
symmetric non-negative matrices. Numerical experiments on simulated graphs
validate the phase transition analysis, and real-world network data is used to
validate the performance of the proposed model selection procedure.Comment: Accepted to IEEE Transactions on Signal Processin
Analysis of Information Delivery Dynamics in Cognitive Sensor Networks Using Epidemic Models
To fully empower sensor networks with cognitive Internet of Things (IoT)
technology, efficient medium access control protocols that enable the
coexistence of cognitive sensor networks with current wireless infrastructure
are as essential as the cognitive power in data fusion and processing due to
shared wireless spectrum. Cognitive radio (CR) is introduced to increase
spectrum efficiency and support such an endeavor, which thereby becomes a
promising building block toward facilitating cognitive IoT. In this paper,
primary users (PUs) refer to devices in existing wireless infrastructure, and
secondary users (SUs) refer to cognitive sensors. For interference control
between PUs and SUs, SUs adopt dynamic spectrum access and power adjustment to
ensure sufficient operation of PUs, which inevitably leads to increasing
latency and poses new challenges on the reliability of IoT communications.
To guarantee operations of primary systems while simultaneously optimizing
system performance in cognitive radio ad hoc networks (CRAHNs), this paper
proposes interference-aware flooding schemes exploiting global timeout and
vaccine recovery schemes to control the heavy buffer occupancy induced by
packet replications. The information delivery dynamics of SUs under the
proposed interference-aware recovery-assisted flooding schemes is analyzed via
epidemic models and stochastic geometry from a macroscopic view of the entire
system. The simulation results show that our model can efficiently capture the
complicated data delivery dynamics in CRAHNs in terms of end-to-end
transmission reliability and buffer occupancy. This paper sheds new light on
analysis of recovery-assisted flooding schemes in CRAHNs and provides
performance evaluation of cognitive IoT services built upon CRAHNs.Comment: 10 page
Universal Phase Transition in Community Detectability under a Stochastic Block Model
We prove the existence of an asymptotic phase transition threshold on
community detectability for the spectral modularity method [M. E. J. Newman,
Phys. Rev. E 74, 036104 (2006) and Proc. National Academy of Sciences. 103,
8577 (2006)] under a stochastic block model. The phase transition on community
detectability occurs as the inter-community edge connection probability
grows. This phase transition separates a sub-critical regime of small ,
where modularity-based community detection successfully identifies the
communities, from a super-critical regime of large where successful
community detection is impossible. We show that, as the community sizes become
large, the asymptotic phase transition threshold is equal to
, where is the within-community edge
connection probability. Thus the phase transition threshold is universal in the
sense that it does not depend on the ratio of community sizes. The universal
phase transition phenomenon is validated by simulations for moderately sized
communities. Using the derived expression for the phase transition threshold we
propose an empirical method for estimating this threshold from real-world data.Comment: 9 pages, 7 figures, to appear in Physical Review
Is Ordered Weighted Regularized Regression Robust to Adversarial Perturbation? A Case Study on OSCAR
Many state-of-the-art machine learning models such as deep neural networks
have recently shown to be vulnerable to adversarial perturbations, especially
in classification tasks. Motivated by adversarial machine learning, in this
paper we investigate the robustness of sparse regression models with strongly
correlated covariates to adversarially designed measurement noises.
Specifically, we consider the family of ordered weighted (OWL)
regularized regression methods and study the case of OSCAR (octagonal shrinkage
clustering algorithm for regression) in the adversarial setting. Under a
norm-bounded threat model, we formulate the process of finding a maximally
disruptive noise for OWL-regularized regression as an optimization problem and
illustrate the steps towards finding such a noise in the case of OSCAR.
Experimental results demonstrate that the regression performance of grouping
strongly correlated features can be severely degraded under our adversarial
setting, even when the noise budget is significantly smaller than the
ground-truth signals.Comment: Accepted to IEEE GlobalSIP 2018. Pin-Yu Chen and Bhanukiran Vinzamuri
contribute equally to this work; v2 fixes missing citatio
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