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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 by completing and reducing graph noise

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Research area:computer-science-aiai-ml

What the study found

The study found that CORE, a data augmentation method for link prediction, is designed to create compact and predictive graph augmentations. The abstract states that it aims to recover missing edges while removing noise from graph structure.

Why the authors say this matters

The authors say link prediction models can lose generalizability because graphs may contain noisy or spurious information and are often incomplete. The study suggests CORE could help make link prediction more robust and improve performance.

What the researchers tested

The researchers proposed COmplete and REduce (CORE) based on the Information Bottleneck principle, a framework for keeping only the most useful information. They evaluated it on multiple benchmark datasets for link prediction in graph representation learning.

What worked and what didn't

The abstract says extensive experiments showed CORE was applicable and superior to state-of-the-art methods. It also states that CORE was intended to both recover missing edges and reduce noise in graph structures.

What to keep in mind

The available summary does not provide detailed experimental settings, specific dataset names, or numerical results. It also does not describe any observed failures or limitations beyond the general problem of noisy and incomplete graphs.

Key points

  • CORE is a data augmentation method for link prediction in graphs.
  • It is designed to recover missing edges and remove noisy graph structure.
  • The method is based on the Information Bottleneck principle.
  • The abstract says experiments on multiple benchmark datasets found CORE superior to state-of-the-art methods.
  • The paper frames graph incompleteness and spurious information as challenges for generalizable link prediction.

Disclosure

Research title:
CORE improves link prediction by completing and reducing graph noise
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:
10.1145/3789200
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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