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This page presents an AI-generated summary of a published research paper. The original authors did not write or review this article. [See full disclosure ↓]

CORE improves link prediction with compact graph augmentations

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Research area:computer-science-aiai-ml

What the study found

The study reports that CORE, short for COmplete and REduce, is a new data augmentation method for link prediction in graph representation learning. The authors say it learns compact and predictive graph augmentations by recovering missing edges while removing noisy or spurious information.

Why the authors say this matters

The authors say this matters because link prediction models can be weakened by noise in graphs and by incomplete graph data. The study suggests CORE may improve robustness and performance for link prediction.

What the researchers tested

The researchers proposed CORE using the Information Bottleneck principle, which is a framework for keeping only the most useful information while discarding unnecessary detail. They evaluated the method on multiple benchmark datasets for link prediction.

What worked and what didn't

The abstract says extensive experiments showed CORE was applicable and superior to state-of-the-art methods on multiple benchmark datasets. It also states that CORE aims to recover missing edges and remove noise, but the abstract does not provide detailed numerical results or describe specific cases where it did not work.

What to keep in mind

The available summary does not give the experimental setup details, dataset names, or performance numbers. It also does not describe specific limitations beyond noting that graphs can contain noisy, spurious, and incomplete information.

Key points

  • CORE is a data augmentation method for link prediction in graph representation learning.
  • The method is designed to recover missing edges and remove noisy or spurious graph information.
  • The authors base CORE on the Information Bottleneck principle.
  • The abstract says experiments on multiple benchmark datasets showed CORE outperformed state-of-the-art methods.
  • The abstract does not report detailed results, dataset names, or specific limitations.

Disclosure

Research title:
CORE improves link prediction with compact graph augmentations
Authors:
Kaiwen Dong, Zhichun Guo, Nitesh V. Chawla
Institutions:
University of Notre Dame, University of Notre Dame, University of Notre Dame
Publication date:
2026-03-05
DOI:
W7133707910
OpenAlex record:
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AI provenance: This post was generated by gpt-5.4-mini (OpenAI). The original authors did not write or review this post.

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