Weisfeiler--Lehman goes Dynamic: An Analysis of the Expressive Power of Graph Neural Networks for Attributed and Dynamic Graphs

Graph Neural Networks (GNNs) are a large class of relational models for graph processing. Recent theoretical studies on the expressive power of GNNs have focused on two issues. On the one hand, it has been proven that GNNs are as powerful as the Weisfeiler-Lehman test (1-WL) in their ability to distinguish graphs. Moreover, it has been shown that the equivalence enforced by 1-WL equals unfolding equivalence. On the other hand, GNNs turned out to be universal approximators on graphs modulo the constraints enforced by 1-WL/unfolding equivalence. However, these results only apply to Static Undirected Homogeneous Graphs with node attributes. In contrast, real-life applications often involve a variety of graph properties, such as, e.g., dynamics or node and edge attributes. In this paper, we conduct a theoretical analysis of the expressive power of GNNs for these two graph types that are particularly of interest. Dynamic graphs are widely used in modern applications, and its theoretical analysis requires new approaches. The attributed type acts as a standard form for all graph types since it has been shown that all graph types can be transformed without loss to Static Undirected Homogeneous Graphs with attributes on nodes and edges (SAUHG). The study considers generic GNN models and proposes appropriate 1-WL tests for those domains. Then, the results on the expressive power of GNNs are extended by proving that GNNs have the same capability as the 1-WL test in distinguishing dynamic and attributed graphs, the 1-WL equivalence equals unfolding equivalence and that GNNs are universal approximators modulo 1-WL/unfolding equivalence. Moreover, the proof of the approximation capability holds for SAUHGs, which include most of those used in practical applications, and it is constructive in nature allowing to deduce hints on the architecture of GNNs that can achieve the desired accuracy

Burden of disease from breast cancer attributable to smoking and second‐hand smoke exposure in Europe

Smoking and second-hand smoke (SHS) exposure have been recently linked to a higher risk of breast cancer in women. The aim of this work is to estimate the number of deaths and disability-adjusted life years (DALYs) from breast cancer attributable to these two risk factors in the European Union (EU-28) in 2017. The comparative risk assessment method was used. Data on prevalence of smoking and SHS exposure were extracted from the Eurobarometer surveys, relative risks from a recent meta-analysis, and data on mortality and DALYs from breast cancer were estimated from the Global Burden of Disease, Injuries and Risk Factors Study. In 2017, 82 239 DALYs and 3354 deaths from breast cancer in the EU-28 could have been avoided by removing exposure to these two risk factors (smoking and SHS exposure). The proportion of DALYs from breast cancer lost respectively from smoking and SHS exposure was 2.6% and 1.0%, although geographically distributed with significant heterogeneity. These results represent the first estimates of breast cancer burden in women attributable to smoking and SHS exposure for the EU-28. It is important to increase awareness among women, health professionals and wider society of the association between smoking, SHS exposure and breast cancer, a relationship that is not widely recognised or discussed

Burden of disease from exposure to secondhand smoke in children in Europe

Background Secondhand smoke (SHS) exposure at home and fetal SHS exposure during pregnancy are a major cause of disease among children. The aim of this study is quantifying the burden of disease due to SHS exposure in children and in pregnancy in 2006–2017 for the 28 European Union (EU) countries. Methods Exposure to SHS was estimated using a multiple imputation procedure based on the Eurobarometer surveys, and SHS exposure burden was estimated with the comparative risk assessment method using meta-analytical relative risks. Data on deaths and disability-adjusted life years (DALYs) were collected from National statistics and from the Global Burden of Disease Study. Results Exposure to SHS and its attributable burden stalled in 2006–2017; in pregnant women, SHS exposure was 19.8% in 2006, 19.1% in 2010, and 21.0% in 2017; in children it was 10.1% in 2006, 9.6% in 2010, and 12.1% in 2017. In 2017, 35,633 DALYs among children were attributable to SHS exposure in the EU, mainly due to low birth weight. Conclusions Comprehensive smoking bans up to 2010 contributed to reduce SHS exposure and its burden in children immediately after their implementation; however, SHS exposure still occurs, and in 2017, its burden in children was still relevant

An annotated timeline of sensitivity analysis

The last half a century has seen spectacular progresses in computing and modelling in a variety of fields, applications, and methodologies. Over the same period, a cross-disciplinary field known as sensitivity analysis has been making its first steps, evolving from the design of experiments for laboratory or field studies, also called ‘in-vivo’, to the so-called experiments ‘in-silico’. Some disciplines were quick to realize the importance of sensitivity analysis, whereas others are still lagging behind. Major tensions within the evolution of this discipline arise from the interplay between local vs global perspectives in the analysis as well as the juxtaposition of the mathematical complexification and the desire for practical applicability. In this work, we retrace these main steps with some attention to the methods and through a bibliometric survey to assess the accomplishments of sensitivity analysis and to identify the potential for its future advancement with a focus on relevant disciplines, such as the environmental field

Burden of disease from breast cancer attributable to smoking and second-hand smoke exposure in Europe

Smoking and second‐hand smoke (SHS) exposure have been recently linked to a higher risk of breast cancer in women. The aim of this work is to estimate the number of deaths and disability‐adjusted life years (DALYs) from breast cancer attributable to these two risk factors in the European Union (EU‐28) in 2017. The comparative risk assessment method was used. Data on prevalence of smoking and SHS exposure were extracted from the Eurobarometer surveys, relative risks from a recent meta‐analysis, and data on mortality and DALYs from breast cancer were estimated from the Global Burden of Disease, Injuries and Risk Factors Study. In 2017, 82 239 DALYs and 3354 deaths from breast cancer in the EU‐28 could have been avoided by removing exposure to these two risk factors (smoking and SHS exposure). The proportion of DALYs from breast cancer lost respectively from smoking and SHS exposure was 2.6% and 1.0%, although geographically distributed with significant heterogeneity. These results represent the first estimates of breast cancer burden in women attributable to smoking and SHS exposure for the EU‐28. It is important to increase awareness among women, health professionals and wider society of the association between smoking, SHS exposure and breast cancer, a relationship that is not widely recognised or discussed

Exposure to secondhand and thirdhand smoke in private vehicles: Measurements in air and dust samples

Background: This study aimed to estimate airborne nicotine concentrations and nicotine, cotinine, and tobaccospecific nitrosamines (TSNAs) in settled dust from private cars in Spain and the UK. Methods: We measured vapor-phase nicotine concentrations in a convenience sample of 45 private cars from Spain (N = 30) and the UK (N = 15) in 2017-2018. We recruited non-smoking drivers (n = 20), smoking drivers who do not smoke inside the car (n = 15), and smoking drivers who smoke inside (n = 10). Nicotine, cotinine, and three TSNAs (NNK, NNN, NNA) were also measured in settled dust in a random subsample (n = 20). We computed medians and interquartile ranges (IQR) of secondhand smoke (SHS) and thirdhand smoke (THS) compounds according to the drivers' profile. Results: 24-h samples yielded median airborne nicotine concentrations below the limit of quantification (LOQ) (IQR