105,099 research outputs found
A simple yet effective baseline for non-attributed graph classification
Graphs are complex objects that do not lend themselves easily to typical
learning tasks. Recently, a range of approaches based on graph kernels or graph
neural networks have been developed for graph classification and for
representation learning on graphs in general. As the developed methodologies
become more sophisticated, it is important to understand which components of
the increasingly complex methods are necessary or most effective.
As a first step, we develop a simple yet meaningful graph representation, and
explore its effectiveness in graph classification. We test our baseline
representation for the graph classification task on a range of graph datasets.
Interestingly, this simple representation achieves similar performance as the
state-of-the-art graph kernels and graph neural networks for non-attributed
graph classification. Its performance on classifying attributed graphs is
slightly weaker as it does not incorporate attributes. However, given its
simplicity and efficiency, we believe that it still serves as an effective
baseline for attributed graph classification. Our graph representation is
efficient (linear-time) to compute. We also provide a simple connection with
the graph neural networks.
Note that these observations are only for the task of graph classification
while existing methods are often designed for a broader scope including node
embedding and link prediction. The results are also likely biased due to the
limited amount of benchmark datasets available. Nevertheless, the good
performance of our simple baseline calls for the development of new, more
comprehensive benchmark datasets so as to better evaluate and analyze different
graph learning methods. Furthermore, given the computational efficiency of our
graph summary, we believe that it is a good candidate as a baseline method for
future graph classification (or even other graph learning) studies.Comment: 13 pages. Shorter version appears at 2019 ICLR Workshop:
Representation Learning on Graphs and Manifolds. arXiv admin note: text
overlap with arXiv:1810.00826 by other author
Graphical review: The redox dark side of e-cigarettes; exposure to oxidants and public health concerns.
Since the initial marketing in 2005, the use of e-cigarettes has increased exponentially. Nonetheless, accumulating evidence has demonstrated the ineffectiveness of e-cigarettes in leading to smoking cessation, and decreasing the adverse health impacts of cigarette smoking. The number of adolescents adapted to e-cigarettes has been increasing substantially each year, and this adaptation has promoted openness to tobacco smoking. The present review discusses controversies regarding the smoking cessation effects of e-cigarettes, recent governmental policies and regulations of e-cigarette use, toxic components and vaporization products of e-cigarettes, and the novel molecular mechanisms underlying the adverse health impacts of e-cigarettes leading to oxidative stress in target tissues, and consequent development of cardiopulmonary diseases (i.e. COPD), neurodegenerative disorders (i.e. Alzheimer's' disease), and cancer. Health warning signs on the packaging and professional consultation to avoid adaptation in risk groups might be helpful solutions to control negative impacts of e-cigarettes. It is also recommended to further expand basic and clinical investigations to reveal more detailed oxidative stress mechanisms of e-cigarette induced damages, which would ultimately result in more effective protective strategies
Study light scalar meson property from heavy meson decays
In the SU(3) symmetry limit, the ratio is
equal to 1 if the scalar mesons are states, while it is 3 if these
mesons are tentraquark states. This ratio provides a model-independent way to
distinguish the descriptions for light scalar mesons . It also applies to the
and decays. The SU(3)
symmetry breaking effect is found to be under control, which will not spoil our
method. The branching fractions of the ,
and decays roughly have the order ,
and , respectively. The B factory experiments and ongoing
BEPC-II experiments are able to measure these channels and accordingly to
provide the detailed information of the scalar meson inner structure.Comment: 5 pages, talk given at 45th Rencontres de Moriond QCD and High Energy
Interactions, March 2010, La Thuile and XIII International Conference on
Hadron Spectroscopy, Florida, 200
Adaptive variance function estimation in heteroscedastic nonparametric regression
We consider a wavelet thresholding approach to adaptive variance function
estimation in heteroscedastic nonparametric regression. A data-driven estimator
is constructed by applying wavelet thresholding to the squared first-order
differences of the observations. We show that the variance function estimator
is nearly optimally adaptive to the smoothness of both the mean and variance
functions. The estimator is shown to achieve the optimal adaptive rate of
convergence under the pointwise squared error simultaneously over a range of
smoothness classes. The estimator is also adaptively within a logarithmic
factor of the minimax risk under the global mean integrated squared error over
a collection of spatially inhomogeneous function classes. Numerical
implementation and simulation results are also discussed.Comment: Published in at http://dx.doi.org/10.1214/07-AOS509 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
We introduce KBGAN, an adversarial learning framework to improve the
performances of a wide range of existing knowledge graph embedding models.
Because knowledge graphs typically only contain positive facts, sampling useful
negative training examples is a non-trivial task. Replacing the head or tail
entity of a fact with a uniformly randomly selected entity is a conventional
method for generating negative facts, but the majority of the generated
negative facts can be easily discriminated from positive facts, and will
contribute little towards the training. Inspired by generative adversarial
networks (GANs), we use one knowledge graph embedding model as a negative
sample generator to assist the training of our desired model, which acts as the
discriminator in GANs. This framework is independent of the concrete form of
generator and discriminator, and therefore can utilize a wide variety of
knowledge graph embedding models as its building blocks. In experiments, we
adversarially train two translation-based models, TransE and TransD, each with
assistance from one of the two probability-based models, DistMult and ComplEx.
We evaluate the performances of KBGAN on the link prediction task, using three
knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental
results show that adversarial training substantially improves the performances
of target embedding models under various settings.Comment: To appear at NAACL HLT 201
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