mlsanityData to 7 Oct 2026

Zero-shot Node Classification with Decomposed Graph Prototype Network

arXiv 2106.08022 ↗First posted 15 Jun 2021cs.AIcited by 15

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Abstract (arXiv)

Node classification is a central task in graph data analysis. Scarce or even no labeled data of emerging classes is a big challenge for existing methods. A natural question arises: can we classify the nodes from those classes that have never been seen? In this paper, we study this zero-shot node classification (ZNC) problem which has a two-stage nature: (1) acquiring high-quality class semantic descriptions (CSDs) for knowledge transfer, and (2) designing a well generalized graph-based learning model. For the first stage, we give a novel quantitative CSDs evaluation strategy based on estimating the real class relationships, so as to get the "best" CSDs in a completely automatic way. For the second stage, we propose a novel Decomposed Graph Prototype Network (DGPN) method, following the principles of locality and compositionality for zero-shot model generalization. Finally, we conduct extensive experiments to demonstrate the effectiveness of our solutions.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 22 of its 53 references that are in the map. First posted on arXiv 15 Jun 2021.

Builds on

22 of 53 references.

References · 22

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10 more
  1. 2018Zero-shot Recognition via Semantic Embeddings and Knowledge Graphscited by 155
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 5; 2024: 5; 2025: 1; 2026: 2.

15 papers.

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  1. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  2. 2022Geometer: Graph Few-Shot Class-Incremental Learning via Prototype Representationcited by 21
  3. 2024Distilling Large Language Models for Text-Attributed Graph Learningcited by 20
  4. 2023A Survey of Imbalanced Learning on Graphs: Problems, Techniques, and Future Directionscited by 15
  5. 2022TopoImb: Toward Topology-level Imbalance in Learning from Graphscited by 12
  6. 2023Edge-free but Structure-aware: Prototype-Guided Knowledge Distillation from GNNs to MLPscited by 7
  7. 2024Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphscited by 6
  8. 2024A Survey of Few-Shot Learning on Graphs: from Meta-Learning to Pre-Training and Prompt Learningcited by 5
  9. 2024NC-NCD: Novel Class Discovery for Node Classificationcited by 4
  10. 2024Hound: Hunting Supervision Signals for Few and Zero Shot Node Classification on Text-attributed Graphcited by 2
  11. 2023Recognizing Unseen Objects via Multimodal Intensive Knowledge Graph Propagationcited by 1
  12. 2023KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classificationcited by 0
3 more
  1. 2025Constraint Multi-class Positive and Unlabeled Learning for Distantly Supervised Named Entity Recognitioncited by 0
  2. 2026Prompt Tuning without Labeled Samples for Zero-Shot Node Classification in Text-Attributed Graphscited by 0
  3. 2026MetaRTL: Meta-path Attention Enhanced Relational Table Learningcited by 0