《计算机信息》杂志发表高质量的论文,扩大了运筹学和计算的范围,寻求有关理论、方法、实验、系统和应用方面的原创研究论文、新颖的调查和教程论文,以及描述新的和有用的软件工具的论文。官网链接:https://pubsonline.informs.org/journal/ijoc

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Lots of learning tasks require dealing with graph data which contains rich relation information among elements. Modeling physics system, learning molecular fingerprints, predicting protein interface, and classifying diseases require that a model to learn from graph inputs. In other domains such as learning from non-structural data like texts and images, reasoning on extracted structures, like the dependency tree of sentences and the scene graph of images, is an important research topic which also needs graph reasoning models. Graph neural networks (GNNs) are connectionist models that capture the dependence of graphs via message passing between the nodes of graphs. Unlike standard neural networks, graph neural networks retain a state that can represent information from its neighborhood with an arbitrary depth. Although the primitive graph neural networks have been found difficult to train for a fixed point, recent advances in network architectures, optimization techniques, and parallel computation have enabled successful learning with them. In recent years, systems based on graph convolutional network (GCN) and gated graph neural network (GGNN) have demonstrated ground-breaking performance on many tasks mentioned above. In this survey, we provide a detailed review over existing graph neural network models, systematically categorize the applications, and propose four open problems for future research.

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Common reporting styles for statistical results, such as $p$-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to null hypothesis testing frameworks. For example, when the $p$-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence in the results. We also asked the respondents' opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations $-$ measured as the "cliff effect": the sudden drop in confidence around $p$-value 0.05 $-$ compared with classic CI visualization and textual representation of the CI with $p$-values. As a contribution to open science, our data and all analyses are publicly available at https://github.com/helske/statvis .

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Common reporting styles for statistical results, such as $p$-values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to null hypothesis testing frameworks. For example, when the $p$-value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. Techniques relying on the visual estimation of the strength of evidence have been recommended to reduce such dichotomous interpretations but their effectiveness has also been challenged. We ran two experiments to compare several alternative representations of confidence intervals and used Bayesian multilevel models to estimate the effects of the representation styles on differences in subjective confidence in the results. We also asked the respondents' opinions and preferences in representation styles. Our results suggest that adding visual information to classic CI representation can decrease the tendency towards dichotomous interpretations $-$ measured as the "cliff effect": the sudden drop in confidence around $p$-value 0.05 $-$ compared with classic CI visualization and textual representation of the CI with $p$-values. As a contribution to open science, our data and all analyses are publicly available at https://github.com/helske/statvis .

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