6,480 research outputs found
Adversarial Attack and Defense on Graph Data: A Survey
Deep neural networks (DNNs) have been widely applied to various applications
including image classification, text generation, audio recognition, and graph
data analysis. However, recent studies have shown that DNNs are vulnerable to
adversarial attacks. Though there are several works studying adversarial attack
and defense strategies on domains such as images and natural language
processing, it is still difficult to directly transfer the learned knowledge to
graph structure data due to its representation challenges. Given the importance
of graph analysis, an increasing number of works start to analyze the
robustness of machine learning models on graph data. Nevertheless, current
studies considering adversarial behaviors on graph data usually focus on
specific types of attacks with certain assumptions. In addition, each work
proposes its own mathematical formulation which makes the comparison among
different methods difficult. Therefore, in this paper, we aim to survey
existing adversarial learning strategies on graph data and first provide a
unified formulation for adversarial learning on graph data which covers most
adversarial learning studies on graph. Moreover, we also compare different
attacks and defenses on graph data and discuss their corresponding
contributions and limitations. In this work, we systemically organize the
considered works based on the features of each topic. This survey not only
serves as a reference for the research community, but also brings a clear image
researchers outside this research domain. Besides, we also create an online
resource and keep updating the relevant papers during the last two years. More
details of the comparisons of various studies based on this survey are
open-sourced at
https://github.com/YingtongDou/graph-adversarial-learning-literature.Comment: In submission to Journal. For more open-source and up-to-date
information, please check our Github repository:
https://github.com/YingtongDou/graph-adversarial-learning-literatur
Advancing Biomedicine with Graph Representation Learning: Recent Progress, Challenges, and Future Directions
Graph representation learning (GRL) has emerged as a pivotal field that has
contributed significantly to breakthroughs in various fields, including
biomedicine. The objective of this survey is to review the latest advancements
in GRL methods and their applications in the biomedical field. We also
highlight key challenges currently faced by GRL and outline potential
directions for future research.Comment: Accepted by 2023 IMIA Yearbook of Medical Informatic
Latent Patient Network Learning for Automatic Diagnosis
Recently, Graph Convolutional Networks (GCNs) has proven to be a powerful
machine learning tool for Computer Aided Diagnosis (CADx) and disease
prediction. A key component in these models is to build a population graph,
where the graph adjacency matrix represents pair-wise patient similarities.
Until now, the similarity metrics have been defined manually, usually based on
meta-features like demographics or clinical scores. The definition of the
metric, however, needs careful tuning, as GCNs are very sensitive to the graph
structure. In this paper, we demonstrate for the first time in the CADx domain
that it is possible to learn a single, optimal graph towards the GCN's
downstream task of disease classification. To this end, we propose a novel,
end-to-end trainable graph learning architecture for dynamic and localized
graph pruning. Unlike commonly employed spectral GCN approaches, our GCN is
spatial and inductive, and can thus infer previously unseen patients as well.
We demonstrate significant classification improvements with our learned graph
on two CADx problems in medicine. We further explain and visualize this result
using an artificial dataset, underlining the importance of graph learning for
more accurate and robust inference with GCNs in medical applications
Differentiable Graph Module (DGM) for Graph Convolutional Networks
Graph deep learning has recently emerged as a powerful ML concept allowing to
generalize successful deep neural architectures to non-Euclidean structured
data. Such methods have shown promising results on a broad spectrum of
applications ranging from social science, biomedicine, and particle physics to
computer vision, graphics, and chemistry. One of the limitations of the
majority of the current graph neural network architectures is that they are
often restricted to the transductive setting and rely on the assumption that
the underlying graph is known and fixed. In many settings, such as those
arising in medical and healthcare applications, this assumption is not
necessarily true since the graph may be noisy, partially- or even completely
unknown, and one is thus interested in inferring it from the data. This is
especially important in inductive settings when dealing with nodes not present
in the graph at training time. Furthermore, sometimes such a graph itself may
convey insights that are even more important than the downstream task. In this
paper, we introduce Differentiable Graph Module (DGM), a learnable function
predicting the edge probability in the graph relevant for the task, that can be
combined with convolutional graph neural network layers and trained in an
end-to-end fashion. We provide an extensive evaluation of applications from the
domains of healthcare (disease prediction), brain imaging (gender and age
prediction), computer graphics (3D point cloud segmentation), and computer
vision (zero-shot learning). We show that our model provides a significant
improvement over baselines both in transductive and inductive settings and
achieves state-of-the-art results
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