Representation learning on networks offers a powerful alternative to the oft painstaking process of manual feature engineering, and, as a result, has enjoyed considerable success in recent years. However, all the existing representation learning methods are based on the first-order network, that is, the network that only captures the pairwise interactions between the nodes. As a result, these methods may fail to incorporate non-Markovian higher order dependencies in the network. Thus, the embeddings that are generated may not accurately represent the underlying phenomena in a network, resulting in inferior performance in different inductive or transductive learning tasks. To address this challenge, this study presents higher order network embedding (HONEM), a higher order network (HON) embedding method that captures the non-Markovian higher order dependencies in a network. HONEM is specifically designed for the HON structure and outperforms other state-of-the-art methods in node classification, network reconstruction, link prediction, and visualization for networks that contain non-Markovian higher order dependencies.

中文翻译：

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HONEM：高阶网络的学习嵌入。

网络上的表示学习为手动特征工程的繁琐过程提供了强大的替代方案，因此近年来取得了相当大的成功。然而，现有的所有表示学习方法都是基于一阶网络，即只捕获节点之间成对交互的网络。因此，这些方法可能无法在网络中合并非马尔可夫高阶依赖关系。因此，生成的嵌入可能无法准确表示网络中的潜在现象，导致在不同的归纳或转导学习任务中表现不佳。为了应对这一挑战，本研究提出了高阶网络嵌入（HONEM），一种高阶网络 (HON) 嵌入方法，可捕获网络中的非马尔可夫高阶依赖关系。HONEM 专为 HON 结构而设计，在包含非马尔可夫高阶依赖关系的网络的节点分类、网络重建、链接预测和可视化方面优于其他最先进的方法。