In real-world scenarios, texts in a network are often linked by multiple semantic relations (e.g., papers in an academic network are referenced by other publications, written by the same author, or published in the same venue), where text documents and their relations form a multiplex text-rich network. Mainstream text representation learning methods use pretrained language models (PLMs) to generate one embedding for each text unit, expecting that all types of relations between texts can be captured by these single-view embeddings. However, this presumption does not hold particularly in multiplex text-rich networks. Along another line of work, multiplex graph neural networks (GNNs) directly initialize node attributes as a feature vector for node representation learning, but they cannot fully capture the semantics of the nodes' associated texts. To bridge these gaps, we propose METERN, a new framework for learning Multiplex Embeddings on TExt-Rich Networks. In contrast to existing methods, METERN uses one text encoder to model the shared knowledge across relations and leverages a small number of parameters per relation to derive relation-specific representations. This allows the encoder to effectively capture the multiplex structures in the network while also preserving parameter efficiency. We conduct experiments on nine downstream tasks in five networks from both academic and e-commerce domains, where METERN outperforms baselines significantly and consistently. The code is available at https://github.com/PeterGriffinJin/METERN-submit.

翻译：在现实场景中，网络中的文本往往通过多种语义关系相互关联（例如，学术网络中的论文被其他出版物引用、由同一作者撰写或发表于同一期刊），这些文本及其关系构成了多重文本富网络。主流文本表示学习方法使用预训练语言模型为每个文本单元生成单一嵌入，期望通过这种单视图嵌入捕获文本间所有类型的关系。然而，这一假设在多重文本富网络中并不成立。另一类研究工作中，多重图神经网络直接将节点属性初始化为特征向量进行节点表示学习，却无法充分捕捉节点关联文本的语义。为弥合这些差距，我们提出METERN——一种面向文本富网络的多重嵌入学习新框架。与现有方法不同，METERN使用单一文本编码器建模跨关系的共享知识，并为每种关系引入少量参数以推导关系专属表示。这使得编码器既能有效捕获网络中的多重结构，又能保持参数效率。我们在学术和电子商务领域的五个网络的九项下游任务上进行实验，结果表明METERN显著且稳定地优于基线方法。代码已开源：https://github.com/PeterGriffinJin/METERN-submit。