Textual network embedding aims to learn low-dimensional representations of
text-annotated nodes in a graph. Prior work in this area has typically focused
on fixed graph structures; however, real-world networks are often dynamic. We
address this challenge with a novel end-to-end node-embedding model, called
Dynamic Embedding for Textual Networks with a Gaussian Process (DetGP). After
training, DetGP can be applied efficiently to dynamic graphs without
re-training or backpropagation. The learned representation of each node is a
combination of textual and structural embeddings. Because the structure is
allowed to be dynamic, our method uses the Gaussian process to take advantage
of its non-parametric properties. To use both local and global graph
structures, diffusion is used to model multiple hops between neighbors. The
relative importance of global versus local structure for the embeddings is
learned automatically. With the non-parametric nature of the Gaussian process,
updating the embeddings for a changed graph structure requires only a forward
pass through the learned model. Considering link prediction and node
classification, experiments demonstrate the empirical effectiveness of our
method compared to baseline approaches. We further show that DetGP can be
straightforwardly and efficiently applied to dynamic textual networks.

本研究提出一种新的文本网络嵌入模型：使用高斯过程的动态嵌入，应用于动态图表征学习中，实现了非参数性的更迭节点嵌入，在节点的表示中结合了文本和结构嵌入，使用扩散模型处理全局和局部结构关系，实验表明，相比基准方法，该方法在链接预测和节点分类上具有更好的实验效果。

基于高斯过程的文本网络动态嵌入

Dynamic Embedding on Textual Networks via a Gaussian Process

Textual network embedding leverages rich text information associated with the
network to learn low-dimensional vectorial representations of vertices. Rather
than using typical natural language processing (NLP) approaches, recent
research exploits the relationship of texts on the same edge to graphically
embed text. However, these models neglect to measure the complete level of
connectivity between any two texts in the graph. We present diffusion maps for
textual network embedding (DMTE), integrating global structural information of
the graph to capture the semantic relatedness between texts, with a
diffusion-convolution operation applied on the text inputs. In addition, a new
objective function is designed to efficiently preserve the high-order proximity
using the graph diffusion. Experimental results show that the proposed approach
outperforms state-of-the-art methods on the vertex-classification and
link-prediction tasks.

采用扩散映射对词语网络嵌入进行建模，以更好地捕捉文本之间的语义联系，并提供一种新的目标函数来有效保留多阶接近度。该方法在顶点分类和链路预测任务上优于最先进的方法。