mlsanityData to 7 Oct 2026

Localized Contrastive Learning on Graphs

arXiv 2212.04604 ↗First posted 8 Dec 2022cs.LGcited by 10

Not on the map. Why?

Abstract (arXiv)

Contrastive learning methods based on InfoNCE loss are popular in node representation learning tasks on graph-structured data. However, its reliance on data augmentation and its quadratic computational complexity might lead to inconsistency and inefficiency problems. To mitigate these limitations, in this paper, we introduce a simple yet effective contrastive model named Localized Graph Contrastive Learning (Local-GCL in short). Local-GCL consists of two key designs: 1) We fabricate the positive examples for each node directly using its first-order neighbors, which frees our method from the reliance on carefully-designed graph augmentations; 2) To improve the efficiency of contrastive learning on graphs, we devise a kernelized contrastive loss, which could be approximately computed in linear time and space complexity with respect to the graph size. We provide theoretical analysis to justify the effectiveness and rationality of the proposed methods. Experiments on various datasets with different scales and properties demonstrate that in spite of its simplicity, Local-GCL achieves quite competitive performance in self-supervised node representation learning tasks on graphs with various scales and properties.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 46 of its 60 references that are in the map. First posted on arXiv 8 Dec 2022.

Builds on

46 of 60 references.

References · 46

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34 more
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 6; 2026: 2.

10 papers.

  1. 2023Simple and Asymmetric Graph Contrastive Learning without Augmentationscited by 37
  2. 2025One Prompt Fits All: Universal Graph Adaptation for Pretrained Modelscited by 11
  3. 2023Self-Pro: A Self-Prompt and Tuning Framework for Graph Neural Networkscited by 8
  4. 2025Node Embeddings via Neighbor Embeddingscited by 1
  5. 2026Why Does Graph Learning Fail to Fully Benefit from a Text Teacher?cited by 1
  6. 2025Simple Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0
  7. 2025Negative Metric Learning for Graphscited by 0
  8. 2025Fractal Graph Contrastive Learningcited by 0
  9. 2025Adaptive Multi-view Graph Contrastive Learning via Fractional-order Neural Diffusion Networkscited by 0
  10. 2026Revisiting Positive Samples in Graph Contrastive Learning: From the Perspective of Message Passingcited by 0