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GraphHop: An Enhanced Label Propagation Method for Node Classification

arXiv 2101.02326 ↗First posted 7 Jan 2021cs.LGcited by 15

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Abstract (arXiv)

A scalable semi-supervised node classification method on graph-structured data, called GraphHop, is proposed in this work. The graph contains attributes of all nodes but labels of a few nodes. The classical label propagation (LP) method and the emerging graph convolutional network (GCN) are two popular semi-supervised solutions to this problem. The LP method is not effective in modeling node attributes and labels jointly or facing a slow convergence rate on large-scale graphs. GraphHop is proposed to its shortcoming. With proper initial label vector embeddings, each iteration of GraphHop contains two steps: 1) label aggregation and 2) label update. In Step 1, each node aggregates its neighbors' label vectors obtained in the previous iteration. In Step 2, a new label vector is predicted for each node based on the label of the node itself and the aggregated label information obtained in Step 1. This iterative procedure exploits the neighborhood information and enables GraphHop to perform well in an extremely small label rate setting and scale well for very large graphs. Experimental results show that GraphHop outperforms state-of-the-art graph learning methods on a wide range of tasks (e.g., multi-label and multi-class classification on citation networks, social graphs, and commodity consumption graphs) in graphs of various sizes. Our codes are publicly available on GitHub (https://github.com/TianXieUSC/GraphHop).

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). Builds on 45 of its 100 references that are in the map. First posted on arXiv 7 Jan 2021.

Builds on

45 of 100 references.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 5; 2023: 1; 2024: 3; 2025: 3; 2026: 2.

15 papers.

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  1. 2024GraphRouter: A Graph-based Router for LLM Selectionscited by 154
  2. 2022Green Learning: Introduction, Examples and Outlookcited by 30
  3. 2022Toward Robust Graph Semi-Supervised Learning against Extreme Data Scarcitycited by 11
  4. 2021E-PixelHop: An Enhanced PixelHop Method for Object Classificationcited by 9
  5. 2024Multi-View Subgraph Neural Networks: Self-Supervised Learning with Scarce Labeled Datacited by 5
  6. 2024Rethinking Independent Cross-Entropy Loss For Graph-Structured Datacited by 4
  7. 2022Label Efficient Regularization and Propagation for Graph Node Classificationcited by 3
  8. 2023Enhancing Edge Intelligence with Highly Discriminant LNT Featurescited by 3
  9. 2025Graph Your Own Promptcited by 2
  10. 2022Learning Optimal Graph Filters for Clustering of Attributed Graphscited by 1
  11. 2022Propagation with Adaptive Mask then Training for Node Classification on Attributed Networkscited by 0
  12. 2025When LLM Agents Meet Graph Optimization: An Automated Data Quality Improvement Approachcited by 0
3 more
  1. 2025ModSSC: A Modular Framework for Semi-Supervised Classification on Heterogeneous Datacited by 0
  2. 2026Learning from Limited Labels: Transductive Graph Label Propagation for Indian Music Analysiscited by 0
  3. 2026OpenRTAG: A Comprehensive Benchmark for Robust Text-Attributed Graph Learning under Data Quality Degradationcited by 0