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

arXiv: https://arxiv.org/abs/2307.11572 · HTML: https://mlsanity.com/paper/2307.11572/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jul 2023.

## Built on by (most cited first, 3 of 3)

- [Label-free Node Classification on Graphs with Large Language Models (LLMS)](https://mlsanity.com/paper/2310.04668/) (2023)
- [Graph Prompt Learning: A Comprehensive Survey and Beyond](https://mlsanity.com/paper/2311.16534/) (2023)
- [Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs](https://mlsanity.com/paper/2412.11983/) (2024)

## Abstract (arXiv)

Multimodal data empowers machine learning models to better understand the world from various perspectives. In this work, we study the combination of 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 Text-And-Graph (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. TAG is a two-stage model with (1) a prompt- and graph-based module which generates prior logits that can be directly used for zero-shot node classification, and (2) a trainable module that further calibrates these prior logits in a few-shot manner. Experiments on two node classification datasets show that TAG outperforms all the baselines by a large margin in both zero- and few-shot settings.