Graph and language embedding models are becoming commonplace in large scale analyses given their ability to represent complex sparse data densely in low-dimensional space. Integrating these models' complementary relational and communicative data may be especially helpful if predicting rare events or classifying members of hidden populations—tasks requiring huge and sparse datasets for generalizable analyses. For example, due to social stigma and comorbidities, mental health support groups often form in amorphous online groups. Predicting suicidality among individuals in these settings using standard network analyses is prohibitive due to resource limits (e.g., memory), and adding auxiliary data like text to such models exacerbates complexity- and sparsity-related issues. Here, I show how merging graph and language embedding models ( metapath2vec and doc2vec ) avoids these limits and extracts unsupervised clustering data without domain expertise or feature enginee...

中文翻译：

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x2vec可以拯救生命吗？集成图和语言嵌入以自动进行心理健康分类

由于图形和语言嵌入模型能够在低维空间中密集表示复杂的稀疏数据，因此在大规模分析中正变得司空见惯。如果预测罕见事件或对隐藏人群的成员进行分类（任务需要庞大且稀疏的数据集以进行可概括的分析），则整合这些模型的互补关系数据和交流数据可能特别有用。例如，由于社会的污名和合并症，精神卫生支持小组经常组成不固定的在线小组。由于资源限制（例如，内存），使用标准网络分析来预测这些环境下个人的自杀倾向是令人望而却步的，向此类模型添加诸如文本之类的辅助数据会加剧与复杂性和稀疏性相关的问题。这里，