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Updated: Sep 19, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Incomplete graph learning: A comprehensive survey.

Riting Xia1, Huibo Liu1, Anchen Li2

  • 1College of Computer Science, Inner Mongolia University, Hohhot, 010021, China.

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|June 15, 2025
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Summary
This summary is machine-generated.

This review introduces incomplete graph learning, addressing challenges in graph data analysis due to missing attributes. It categorizes methods and discusses future research directions for robust graph learning.

Keywords:
Attribute-incomplete graphsAttribute-missing graphsGraph learningIncomplete graph learningIncomplete graphsRobustness

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Area of Science:

  • Graph learning
  • Data science
  • Machine learning

Background:

  • Graph learning methods are vital for extracting information from graph data.
  • Existing methods lack robustness when dealing with missing attributes, leading to suboptimal performance.
  • Incomplete graph learning addresses the challenge of learning from incomplete graph data.

Purpose of the Study:

  • To provide a comprehensive review of the literature on incomplete graph learning.
  • To categorize incomplete graphs and define key concepts and techniques.
  • To classify existing methods based on types of incompleteness.

Main Methods:

  • Systematic literature review of incomplete graph learning.
  • Categorization of incomplete graphs and learning methods (attribute-incomplete, attribute-missing, hybrid-absent).
  • Summarization of datasets, processing modes, evaluation metrics, and application domains.

Main Results:

  • Classification of incomplete graph learning methods based on attribute incompleteness.
  • Identification of commonalities and differences among existing approaches.
  • Summary of resources and methodologies used in the field.

Conclusions:

  • This is the first review dedicated to incomplete graph learning.
  • Highlights challenges and proposes future research directions.
  • Aims to provide valuable insights for researchers in graph learning and related fields.