This paper is a quantitative analysis of the data collected globally by the
World Value Survey. The data is used to study the trajectories of change in
individuals' religious beliefs, values, and behaviors in societies. Utilizing
random forest, we aim to identify the key factors of religiosity and classify
respondents of the survey as religious and non religious using country level
data. We use resampling techniques to balance the data and improve imbalanced
learning performance metrics. The results of the variable importance analysis
suggest that Age and Income are the most important variables in the majority of
countries. The results are discussed with fundamental sociological theories
regarding religion and human behavior. This study is an application of machine
learning in identifying the underlying patterns in the data of 30 countries
participating in the World Value Survey. The results from variable importance
analysis and classification of imbalanced data provide valuable insights
beneficial to theoreticians and researchers of social sciences.

该研究利用全球通过世界价值观调查收集的数据进行定量分析，研究了社会中个体宗教信仰、价值观和行为的变化轨迹。利用随机森林，通过国家级数据识别出宗教性的关键因素，并将调查中的受访者分类为宗教和非宗教。通过平衡数据和改进不平衡学习性能指标的重采样技术，变量重要性分析结果表明年龄和收入是大多数国家中最重要的变量。研究结果与有关宗教和人类行为的基本社会学理论进行了讨论。该研究是在世界价值观调查的 30 个参与国家的数据中识别潜在模式的机器学习应用。变量重要性分析和不平衡数据分类的结果为社会科学的理论家和研究人员提供了有价值的洞察。

二十一世纪的宗教隶属关系：基于机器学习的世界价值观调查视角

Religious Affiliation in the Twenty-First Century: A Machine Learning  Perspective on the World Value Survey

Network embedding is aimed at mapping nodes in a network into low-dimensional
vector representations. Graph Neural Networks (GNNs) have received widespread
attention and lead to state-of-the-art performance in learning node
representations. However, most GNNs only work in unsigned networks, where only
positive links exist. It is not trivial to transfer these models to signed
directed networks, which are widely observed in the real world yet less
studied. In this paper, we first review two fundamental sociological theories
(i.e., status theory and balance theory) and conduct empirical studies on
real-world datasets to analyze the social mechanism in signed directed
networks. Guided by related sociological theories, we propose a novel Signed
Directed Graph Neural Networks model named SDGNN to learn node embeddings for
signed directed networks. The proposed model simultaneously reconstructs link
signs, link directions, and signed directed triangles. We validate our model's
effectiveness on five real-world datasets, which are commonly used as the
benchmark for signed network embedding. Experiments demonstrate the proposed
model outperforms existing models, including feature-based methods, network
embedding methods, and several GNN methods.

本文提出了一种名为 SDGNN 的有向图神经网络模型，根据社会理论在有向网络中学习节点嵌入表示，并同时重建了链接的符号、方向和符号三角形。实验证明，该模型优于现有的基于特征和基于网络嵌入、基于 GNN 方法的模型。