Improving Expressivity of GNNs with Subgraph-specific Factor Embedded Normalization

Chen, Kaixuan; Liu, Shunyu; Zhu, Tongtian; Qiao, Junfei; Su, Yun; Tian, Yingjie; Zheng, Tongya; Zhang, H; Feng, Zunlei; Song, Mingli

doi:10.1145/3580305.3599388

Cited by 3 publications

(1 citation statement)

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“…Therefore, they devise a novel normalization method named GraphNorm that automatically adjusts the step of shift operation to preserve the graph structure information. Besides Cai's work, there are some other graph-specific normalization methods aiming to deal with problems in graph representation learning such as over-smoothing and varying graph size [202,203,204,205,206,207]. Very Recently, Eliasof et al [208] introduce GRANOLA that adaptively perform normalization on node feature according to the input graph via attaching random feature that we mention in Section 3 and then passing through an additional GNN, which not only enhances the performance of GNNs across various graph benchmarks and architectures, but also increases the expressive power of the GNN model.…”

Section: Training Tricksmentioning

confidence: 99%

The prevalence and characteristics of metabolic syndrome according to different definitions in China: a nationwide cross-sectional study, 2012–2015

et al. 2022

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Background Metabolic syndrome (MetS) is characterized by a cluster of signs of metabolic disturbance and has caused a huge burden on the health system. The study aims to explore the prevalence and characteristics of MetS defined by different criteria in the Chinese population. Methods Using the data of the China Hypertension Survey (CHS), a nationally representative cross-sectional study from October 2012 to December 2015, a total of 28,717 participants aged 35 years and above were included in the analysis. The MetS definitions of the International Diabetes Federation (IDF), the updated US National Cholesterol Education Program Adult Treatment Panel III (the revised ATP III), and the Joint Committee for Developing Chinese Guidelines (JCDCG) on Prevention and Treatment of Dyslipidemia in Adults were used. Multivariable logistic regression was used to identify factors associated with MetS. Results The prevalence of MetS diagnosed according to the definitions of IDF, the revised ATP III, and JCCDS was 26.4%, 32.3%, and 21.5%, respectively. The MetS prevalence in men was lower than in women by IDF definition (22.2% vs. 30.3%) and by the revised ATP III definition (29.2% vs. 35.4%), but the opposite was true by JCDCG (24.4%vs 18.5%) definition. The consistency between the three definitions for men and the revised ATP III definition and IDF definition for women was relatively good, with kappa values ranging from 0.77 to 0.89, but the consistency between the JCDCG definition and IDF definition (kappa = 0.58) and revised ATP III definition (kappa = 0.58) was poor. Multivariable logistic regression showed that although the impact and correlation intensity varied with gender and definition, area, age, education, smoking, alcohol use, and family history of cardiovascular disease were factors related to MetS. Conclusions The prevalence and characteristics of the MetS vary with the definition used in the Chinese population. The three MetS definitions are more consistent in men but relatively poor in women. On the other hand, even if estimated according to the definition of the lowest prevalence, MetS is common in China.

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Section: Training Tricksmentioning

confidence: 99%