Learning Multiplex Representations on Text-Attributed Graphs with One Language Model Encoder

TL;DR

This paper introduces METAG, a framework that uses a single language model encoder with relation-specific parameters to effectively learn multiplex representations on text-attributed graphs, outperforming existing methods.

Contribution

METAG is the first approach to combine a shared language model encoder with relation-specific parameters for multiplex graph representation learning.

Findings

METAG outperforms baseline methods on nine downstream tasks.

The framework effectively captures multiplex structures with parameter efficiency.

Experiments demonstrate significant and consistent improvements across multiple domains.

Abstract

In real-world scenarios, texts in a graph are often linked by multiple semantic relations (e.g., papers in an academic graph 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-attributed graph. 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-attributed graphs. 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 METAG, a new framework for learning Multiplex rEpresentations on Text-Attributed Graphs. In contrast to existing methods, METAG 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 graph while also preserving parameter efficiency. We conduct experiments on nine downstream tasks in five graphs from both academic and e-commerce domains, where METAG outperforms baselines significantly and consistently. The code is available at https://github.com/PeterGriffinJin/METAG.