Graph convolutional networks (GCNs) have achieved great success in many applications and have caught significant attention in both academic and industrial domains. However, repeatedly employing graph convolutional layers would render the node embeddings indistinguishable. For the sake of avoiding oversmoothing, most GCN-based models are restricted in a shallow architecture. Therefore, the expressive power of these models is insufficient since they ignore information beyond local neighborhoods. Furthermore, existing methods either do not consider the semantics from high-order local structures or neglect the node homophily (i.e., node similarity), which severely limits the performance of the model. In this article, we take above problems into consideration and propose a novel Semantics and Homophily preserving Network Embedding (SHNE) model. In particular, SHNE leverages higher order connectivity patterns to capture structural semantics. To exploit node homophily, SHNE utilizes both structural and feature similarity to discover potential correlated neighbors for each node from the whole graph; thus, distant but informative nodes can also contribute to the model. Moreover, with the proposed dual-attention mechanisms, SHNE learns comprehensive embeddings with additional information from various semantic spaces. Furthermore, we also design a semantic regularizer to improve the quality of the combined representation. Extensive experiments demonstrate that SHNE outperforms state-of-the-art methods on benchmark datasets.

中文翻译：

---

SHNE：语义和同质性保留网络嵌入

图卷积网络（GCN）在许多应用中取得了巨大成功，并引起了学术和工业领域的广泛关注。然而，重复使用图卷积层会使节点嵌入无法区分。为了避免过度平滑，大多数基于 GCN 的模型都限制在浅层架构中。因此，这些模型的表达能力不足，因为它们忽略了本地社区之外的信息。此外，现有方法要么不考虑高阶局部结构的语义，要么忽略节点同质性（即节点相似性），这严重限制了模型的性能。在本文中，我们考虑到上述问题，提出了一种新颖的语义和同质性保留网络嵌入（SHNE）模型。特别是，SHNE 利用高阶连接模式来捕获结​​构语义。为了利用节点同质性，SHNE 利用结构和特征相似性来发现整个图中每个节点的潜在相关邻居；因此，遥远但信息丰富的节点也可以对模型做出贡献。此外，通过所提出的双重注意力机制，SHNE 可以学习来自各种语义空间的附加信息的综合嵌入。此外，我们还设计了一个语义正则化器来提高组合表示的质量。大量实验表明，SHNE 在基准数据集上的性能优于最先进的方法。