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Updated: Jul 28, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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Collaborative bi-aggregation for directed graph embedding.

Linsong Liu1, Ke-Jia Chen2, Zheng Liu2

  • 1School of Computer Science, Nanjing University of Posts and Telecommunications, Nanjing, 210023, Jiangsu, China.

Neural Networks : the Official Journal of the International Neural Network Society
|June 2, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a collaborative bi-directional aggregation method (COBA) for directed graph embedding. COBA effectively addresses challenges with low-degree nodes, outperforming existing methods in graph analysis tasks.

Keywords:
Bi-directional aggregationDirected graphGraph representation learningLink prediction

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Area of Science:

  • Computer Science
  • Graph Theory
  • Machine Learning

Background:

  • Directed graphs model asymmetric relationships, crucial for graph analysis.
  • Current directed graph embedding methods struggle with low/zero in/out-degree nodes in sparse graphs.

Purpose of the Study:

  • Propose a novel collaborative bi-directional aggregation method (COBA) for directed graph embedding.
  • Enhance representation learning for nodes with limited connectivity.

Main Methods:

  • COBA aggregates source/target embeddings from neighbors.
  • It specifically enhances zero in/out-degree nodes using opposite-directional neighbors.
  • Source and target embeddings are correlated for collaborative aggregation.

Main Results:

  • Theoretical analysis confirms COBA's feasibility and rationality.
  • Experiments show COBA outperforms state-of-the-art methods on multiple tasks.
  • Effectiveness of the proposed aggregation strategies is validated.

Conclusions:

  • COBA offers a robust solution for directed graph embedding, particularly for sparse graphs.
  • The method improves downstream graph analysis and inference capabilities.