82,900 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
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