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 $R\equiv\frac{{\cal B}(D^+\to f_0l^+\nu)+ {\cal B}(D^+\to \sigma l^+\nu)}{{\cal B}(D^+\to a_0^0l^+\nu)}$ is equal to 1 if the scalar mesons are $\bar qq$ 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 $B^-\to Sl^-\bar\nu$ and $\bar B^0\to J/\psi(\eta_c) S$ decays. The SU(3) symmetry breaking effect is found to be under control, which will not spoil our method. The branching fractions of the $D^+\to S l^+\nu$, $B^-\to S l^-\bar\nu$ and $\bar B^0\to J/\psi(\eta_c) S$ decays roughly have the order $10^{-4}$, $10^{-5}$ and $10^{-6}$, 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