3,941 research outputs found
Representation Learning on Graphs: Methods and Applications
Machine learning on graphs is an important and ubiquitous task with
applications ranging from drug design to friendship recommendation in social
networks. The primary challenge in this domain is finding a way to represent,
or encode, graph structure so that it can be easily exploited by machine
learning models. Traditionally, machine learning approaches relied on
user-defined heuristics to extract features encoding structural information
about a graph (e.g., degree statistics or kernel functions). However, recent
years have seen a surge in approaches that automatically learn to encode graph
structure into low-dimensional embeddings, using techniques based on deep
learning and nonlinear dimensionality reduction. Here we provide a conceptual
review of key advancements in this area of representation learning on graphs,
including matrix factorization-based methods, random-walk based algorithms, and
graph neural networks. We review methods to embed individual nodes as well as
approaches to embed entire (sub)graphs. In doing so, we develop a unified
framework to describe these recent approaches, and we highlight a number of
important applications and directions for future work.Comment: Published in the IEEE Data Engineering Bulletin, September 2017;
version with minor correction
Deep Representation Learning for Social Network Analysis
Social network analysis is an important problem in data mining. A fundamental
step for analyzing social networks is to encode network data into
low-dimensional representations, i.e., network embeddings, so that the network
topology structure and other attribute information can be effectively
preserved. Network representation leaning facilitates further applications such
as classification, link prediction, anomaly detection and clustering. In
addition, techniques based on deep neural networks have attracted great
interests over the past a few years. In this survey, we conduct a comprehensive
review of current literature in network representation learning utilizing
neural network models. First, we introduce the basic models for learning node
representations in homogeneous networks. Meanwhile, we will also introduce some
extensions of the base models in tackling more complex scenarios, such as
analyzing attributed networks, heterogeneous networks and dynamic networks.
Then, we introduce the techniques for embedding subgraphs. After that, we
present the applications of network representation learning. At the end, we
discuss some promising research directions for future work
Can Graph Neural Networks Go "Online"? An Analysis of Pretraining and Inference
Large-scale graph data in real-world applications is often not static but
dynamic, i. e., new nodes and edges appear over time. Current graph convolution
approaches are promising, especially, when all the graph's nodes and edges are
available during training. When unseen nodes and edges are inserted after
training, it is not yet evaluated whether up-training or re-training from
scratch is preferable. We construct an experimental setup, in which we insert
previously unseen nodes and edges after training and conduct a limited amount
of inference epochs. In this setup, we compare adapting pretrained graph neural
networks against retraining from scratch. Our results show that pretrained
models yield high accuracy scores on the unseen nodes and that pretraining is
preferable over retraining from scratch. Our experiments represent a first step
to evaluate and develop truly online variants of graph neural networks.Comment: 5 pages, 1 figure, Representation Learning on Graphs and Manifolds
Workshop of the International Conference on Learning Representations (ICLR),
201
Adversarial Defense Framework for Graph Neural Network
Graph neural network (GNN), as a powerful representation learning model on
graph data, attracts much attention across various disciplines. However, recent
studies show that GNN is vulnerable to adversarial attacks. How to make GNN
more robust? What are the key vulnerabilities in GNN? How to address the
vulnerabilities and defense GNN against the adversarial attacks? In this paper,
we propose DefNet, an effective adversarial defense framework for GNNs. In
particular, we first investigate the latent vulnerabilities in every layer of
GNNs and propose corresponding strategies including dual-stage aggregation and
bottleneck perceptron. Then, to cope with the scarcity of training data, we
propose an adversarial contrastive learning method to train the GNN in a
conditional GAN manner by leveraging the high-level graph representation.
Extensive experiments on three public datasets demonstrate the effectiveness of
DefNet in improving the robustness of popular GNN variants, such as Graph
Convolutional Network and GraphSAGE, under various types of adversarial
attacks
Deep Structured Models For Group Activity Recognition
This paper presents a deep neural-network-based hierarchical graphical model
for individual and group activity recognition in surveillance scenes. Deep
networks are used to recognize the actions of individual people in a scene.
Next, a neural-network-based hierarchical graphical model refines the predicted
labels for each class by considering dependencies between the classes. This
refinement step mimics a message-passing step similar to inference in a
probabilistic graphical model. We show that this approach can be effective in
group activity recognition, with the deep graphical model improving recognition
rates over baseline methods
Graph Convolutional Reinforcement Learning
Learning to cooperate is crucially important in multi-agent environments. The
key is to understand the mutual interplay between agents. However, multi-agent
environments are highly dynamic, where agents keep moving and their neighbors
change quickly. This makes it hard to learn abstract representations of mutual
interplay between agents. To tackle these difficulties, we propose graph
convolutional reinforcement learning, where graph convolution adapts to the
dynamics of the underlying graph of the multi-agent environment, and relation
kernels capture the interplay between agents by their relation representations.
Latent features produced by convolutional layers from gradually increased
receptive fields are exploited to learn cooperation, and cooperation is further
improved by temporal relation regularization for consistency. Empirically, we
show that our method substantially outperforms existing methods in a variety of
cooperative scenarios.Comment: ICLR'2
A Survey on Deep Learning for Neuroimaging-based Brain Disorder Analysis
Deep learning has been recently used for the analysis of neuroimages, such as
structural magnetic resonance imaging (MRI), functional MRI, and positron
emission tomography (PET), and has achieved significant performance
improvements over traditional machine learning in computer-aided diagnosis of
brain disorders. This paper reviews the applications of deep learning methods
for neuroimaging-based brain disorder analysis. We first provide a
comprehensive overview of deep learning techniques and popular network
architectures, by introducing various types of deep neural networks and recent
developments. We then review deep learning methods for computer-aided analysis
of four typical brain disorders, including Alzheimer's disease, Parkinson's
disease, Autism spectrum disorder, and Schizophrenia, where the first two
diseases are neurodegenerative disorders and the last two are
neurodevelopmental and psychiatric disorders, respectively. More importantly,
we discuss the limitations of existing studies and present possible future
directions.Comment: 30 pages, 7 figure
ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
The chest X-ray is one of the most commonly accessible radiological
examinations for screening and diagnosis of many lung diseases. A tremendous
number of X-ray imaging studies accompanied by radiological reports are
accumulated and stored in many modern hospitals' Picture Archiving and
Communication Systems (PACS). On the other side, it is still an open question
how this type of hospital-size knowledge database containing invaluable imaging
informatics (i.e., loosely labeled) can be used to facilitate the data-hungry
deep learning paradigms in building truly large-scale high precision
computer-aided diagnosis (CAD) systems.
In this paper, we present a new chest X-ray database, namely "ChestX-ray8",
which comprises 108,948 frontal-view X-ray images of 32,717 unique patients
with the text-mined eight disease image labels (where each image can have
multi-labels), from the associated radiological reports using natural language
processing. Importantly, we demonstrate that these commonly occurring thoracic
diseases can be detected and even spatially-located via a unified
weakly-supervised multi-label image classification and disease localization
framework, which is validated using our proposed dataset. Although the initial
quantitative results are promising as reported, deep convolutional neural
network based "reading chest X-rays" (i.e., recognizing and locating the common
disease patterns trained with only image-level labels) remains a strenuous task
for fully-automated high precision CAD systems. Data download link:
https://nihcc.app.box.com/v/ChestXray-NIHCCComment: CVPR 2017 spotlight;V1: CVPR submission+supplementary; V2: Statistics
and benchmark results on published ChestX-ray14 dataset are updated in
Appendix B V3: Minor correction V4: new data download link upated:
https://nihcc.app.box.com/v/ChestXray-NIHCC V5: Update benchmark results on
the published data split in the appendi
A Collaborative Computer Aided Diagnosis (C-CAD) System with Eye-Tracking, Sparse Attentional Model, and Deep Learning
There are at least two categories of errors in radiology screening that can
lead to suboptimal diagnostic decisions and interventions:(i)human fallibility
and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are
developed to help radiologists to compensate for some of these errors. However,
despite their significant improvements over conventional screening strategies,
most CAD systems do not go beyond their use as second opinion tools due to
producing a high number of false positives, which human interpreters need to
correct. In parallel with efforts in computerized analysis of radiology scans,
several researchers have examined behaviors of radiologists while screening
medical images to better understand how and why they miss tumors, how they
interact with the information in an image, and how they search for unknown
pathology in the images. Eye-tracking tools have been instrumental in exploring
answers to these fundamental questions. In this paper, we aim to develop a
paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both
of the above mentioned research lines: CAD and eye-tracking. We design an
eye-tracking interface providing radiologists with a real radiology reading
room experience. Then, we propose a novel algorithm that unifies eye-tracking
data and a CAD system. Specifically, we present a new graph based clustering
and sparsification algorithm to transform eye-tracking data (gaze) into a
signal model to interpret gaze patterns quantitatively and qualitatively. The
proposed C-CAD collaborates with radiologists via eye-tracking technology and
helps them to improve diagnostic decisions. The C-CAD learns radiologists'
search efficiency by processing their gaze patterns. To do this, the C-CAD uses
a deep learning algorithm in a newly designed multi-task learning platform to
segment and diagnose cancers simultaneously.Comment: Submitted to Medical Image Analysis Journal (MedIA
Structure Inference Net: Object Detection Using Scene-Level Context and Instance-Level Relationships
Context is important for accurate visual recognition. In this work we propose
an object detection algorithm that not only considers object visual appearance,
but also makes use of two kinds of context including scene contextual
information and object relationships within a single image. Therefore, object
detection is regarded as both a cognition problem and a reasoning problem when
leveraging these structured information. Specifically, this paper formulates
object detection as a problem of graph structure inference, where given an
image the objects are treated as nodes in a graph and relationships between the
objects are modeled as edges in such graph. To this end, we present a so-called
Structure Inference Network (SIN), a detector that incorporates into a typical
detection framework (e.g. Faster R-CNN) with a graphical model which aims to
infer object state. Comprehensive experiments on PASCAL VOC and MS COCO
datasets indicate that scene context and object relationships truly improve the
performance of object detection with more desirable and reasonable outputs.Comment: published in CVPR 201
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