1,246 research outputs found
Learn to Generate Time Series Conditioned Graphs with Generative Adversarial Nets
Deep learning based approaches have been utilized to model and generate
graphs subjected to different distributions recently. However, they are
typically unsupervised learning based and unconditioned generative models or
simply conditioned on the graph-level contexts, which are not associated with
rich semantic node-level contexts. Differently, in this paper, we are
interested in a novel problem named Time Series Conditioned Graph Generation:
given an input multivariate time series, we aim to infer a target relation
graph modeling the underlying interrelationships between time series with each
node corresponding to each time series. For example, we can study the
interrelationships between genes in a gene regulatory network of a certain
disease conditioned on their gene expression data recorded as time series. To
achieve this, we propose a novel Time Series conditioned Graph
Generation-Generative Adversarial Networks (TSGG-GAN) to handle challenges of
rich node-level context structures conditioning and measuring similarities
directly between graphs and time series. Extensive experiments on synthetic and
real-word gene regulatory networks datasets demonstrate the effectiveness and
generalizability of the proposed TSGG-GAN
Synthetic Observational Health Data with GANs: from slow adoption to a boom in medical research and ultimately digital twins?
After being collected for patient care, Observational Health Data (OHD) can
further benefit patient well-being by sustaining the development of health
informatics and medical research. Vast potential is unexploited because of the
fiercely private nature of patient-related data and regulations to protect it.
Generative Adversarial Networks (GANs) have recently emerged as a
groundbreaking way to learn generative models that produce realistic synthetic
data. They have revolutionized practices in multiple domains such as
self-driving cars, fraud detection, digital twin simulations in industrial
sectors, and medical imaging.
The digital twin concept could readily apply to modelling and quantifying
disease progression. In addition, GANs posses many capabilities relevant to
common problems in healthcare: lack of data, class imbalance, rare diseases,
and preserving privacy. Unlocking open access to privacy-preserving OHD could
be transformative for scientific research. In the midst of COVID-19, the
healthcare system is facing unprecedented challenges, many of which of are data
related for the reasons stated above.
Considering these facts, publications concerning GAN applied to OHD seemed to
be severely lacking. To uncover the reasons for this slow adoption, we broadly
reviewed the published literature on the subject. Our findings show that the
properties of OHD were initially challenging for the existing GAN algorithms
(unlike medical imaging, for which state-of-the-art model were directly
transferable) and the evaluation synthetic data lacked clear metrics.
We find more publications on the subject than expected, starting slowly in
2017, and since then at an increasing rate. The difficulties of OHD remain, and
we discuss issues relating to evaluation, consistency, benchmarking, data
modelling, and reproducibility.Comment: 31 pages (10 in previous version), not including references and
glossary, 51 in total. Inclusion of a large number of recent publications and
expansion of the discussion accordingl
A Systematic Survey on Deep Generative Models for Graph Generation
Graphs are important data representations for describing objects and their
relationships, which appear in a wide diversity of real-world scenarios. As one
of a critical problem in this area, graph generation considers learning the
distributions of given graphs and generating more novel graphs. Owing to its
wide range of applications, generative models for graphs have a rich history,
which, however, are traditionally hand-crafted and only capable of modeling a
few statistical properties of graphs. Recent advances in deep generative models
for graph generation is an important step towards improving the fidelity of
generated graphs and paves the way for new kinds of applications. This article
provides an extensive overview of the literature in the field of deep
generative models for the graph generation. Firstly, the formal definition of
deep generative models for the graph generation as well as preliminary
knowledge is provided. Secondly, two taxonomies of deep generative models for
unconditional, and conditional graph generation respectively are proposed; the
existing works of each are compared and analyzed. After that, an overview of
the evaluation metrics in this specific domain is provided. Finally, the
applications that deep graph generation enables are summarized and five
promising future research directions are highlighted
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