Prompt-Based Zero- and Few-Shot Node Classification: A Multimodal Approach

Understand

Multimodal data empowers machine learning models to better understand the world from various perspectives.

• In this work, we study the combination of \emph{text and graph} modalities, a challenging but understudied combination which is prevalent across multiple settings including citation networks, social media, and the web.
• We focus on the popular task of node classification using limited labels; in particular, under the zero- and few-shot scenarios.
• In contrast to the standard pipeline which feeds standard precomputed (e.g., bag-of-words) text features into a graph neural network, we propose \textbf{T}ext-\textbf{A}nd-\textbf{G}raph (TAG) learning, a more deeply multimodal approach that integrates the raw texts and graph topology into the model design, and can effectively learn from limited supervised signals without any meta-learning procedure.