The performance of many network learning applications crucially hinges on the success of network embedding algorithms, which aim to encode rich network information into low-dimensional vertex-based vector representations. This paper considers a novel variational formulation of network embeddings, with special focus on textual networks. Different from most existing methods that optimize a discriminative objective, we introduce Variational Homophilic Embedding (VHE), a fully generative model that learns network embeddings by modeling the semantic (textual) information with a variational autoencoder, while accounting for the structural (topology) information through a novel homophilic prior design. Homophilic vertex embeddings encourage similar embedding vectors for related (connected) vertices. The proposed VHE promises better generalization for downstream tasks, robustness to incomplete observations, and the ability to generalize to unseen vertices. Extensive experiments on real-world networks, for multiple tasks, demonstrate that the proposed method consistently achieves superior performance relative to competing state-of-the-art approaches.

本文提出了一种新颖的变分网络嵌入形式，称为变分同质嵌入，它通过模拟语义信息和结构信息来学习网络嵌入，鼓励相关顶点的相似嵌入向量，具有更好的下游任务泛化性能，对不完整观测的鲁棒性以及推广到未知顶点的能力，并通过实验结果证明了其在多个任务方面相对于其他竞争最新方法的卓越表现。

使用变分同配嵌入改进文本网络学习