In graphs with rich texts, incorporating textual information with structural
information would benefit constructing expressive graph embeddings. Among
various graph embedding models, random walk (RW)-based is one of the most
popular and successful groups. However, it is challenged by two issues when
applied on graphs with rich texts: (i) sampling efficiency: deriving from the
training objective of RW-based models (e.g., DeepWalk and node2vec), we show
that RW-based models are likely to generate large amounts of redundant training
samples due to three main drawbacks. (ii) text utilization: these models have
difficulty in dealing with zero-shot scenarios where graph embedding models
have to infer graph structures directly from texts. To solve these problems, we
propose a novel framework, namely Text-driven Graph Embedding with Pairs
Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling
strategy of RW, being able to reduce ~99% training samples while preserving
competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an
inductive graph embedding approach, to generate node embeddings from texts.
Since each node contains rich texts, TGE is able to generate high-quality
embeddings and provide reasonable predictions on existence of links to unseen
nodes. We evaluate TGE-PS on several real-world datasets, and experiment
results demonstrate that TGE-PS produces state-of-the-art results on both
traditional and zero-shot link prediction tasks.

提出一种名为 TGE-PS 的新框架，通过使用 Pairs Sampling 来改善 RW 的采样策略，在传统和零样本链接预测任务中取得最先进的结果。该框架中采用 TGE 进行文本驱动的图嵌入，从而解决了 RW 模型在处理富文本图时的采样效率和零样本场景下无法推断图结构等问题。