Since real-world objects and their interactions are often multi-modal and multi-typed, heterogeneous networks have been widely used as a more powerful, realistic, and generic superclass of traditional homogeneous networks (graphs). Meanwhile, representation learning (\aka~embedding) has recently been intensively studied and shown effective for various network mining and analytical tasks. Since there has already been a broad body of heterogeneous network embedding (HNE) algorithms but no dedicated survey, as the first contribution of this work, we pioneer in providing a unified paradigm for the systematic categorization and analysis over the merits of various existing HNE algorithms. Moreover, existing HNE algorithms, though mostly claimed generic, are often evaluated on different datasets. Understandable due to the natural application favor of HNE, such indirect comparisons largely hinder the proper attribution of improved task performance towards effective data preprocessing and novel technical design, especially considering the various ways possible to construct a heterogeneous network from real-world application data. Therefore, as the second contribution, we create four benchmark datasets with various properties regarding scale, structure, attribute/label availability, and \etc.~from different sources, towards the comprehensive evaluation of HNE algorithms. As the third contribution, we carefully refactor and amend the implementations of and create friendly interfaces for ten popular HNE algorithms, and provide all-around comparisons among them over multiple tasks and experimental settings.

本研究旨在提供一个统一框架以深入概述和评估异构网络嵌入(HNE)的现有研究，并通过提供分类和分析不同现有HNE算法的优点和缺点、创建四个基准数据集以便于对HNE算法进行公正的评价、以及修改并创建13个受欢迎的HNE算法的友好界面，并在多个任务和实验设置中进行全方位比较来推动深度学习在异质网络关键任务中的应用。

异构网络表示学习: 一种具有调研和基准的统一框架