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Updated: Jul 19, 2025

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
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GATCF: Graph Attention Collaborative Filtering for Reliable Blockchain Services Selection in BaaS.

Yuxiang Zeng1, Jianlong Xu1, Zhuohua Zhang1

  • 1College of Engineering, Shantou University, Shantou 515063, China.

Sensors (Basel, Switzerland)
|August 12, 2023
PubMed
Summary
This summary is machine-generated.

Selecting reliable blockchain peers is challenging due to data sparsity. Our Graph Attention Collaborative Filtering (GATCF) model effectively addresses this by leveraging graph attention and collaborative filtering for improved peer selection.

Keywords:
blockchain servicescollaborative filteringgraph attentionreliability prediction

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Area of Science:

  • Computer Science
  • Distributed Systems

Background:

  • Blockchain as a Service (BaaS) simplifies blockchain application development.
  • Numerous BaaS options with overlapping features complicate reliable peer selection.
  • Data sparsity is a significant challenge in identifying trusted blockchain peers.

Purpose of the Study:

  • To propose a novel model for selecting reliable blockchain peers.
  • To address the data sparsity issue in Blockchain as a Service (BaaS) environments.

Main Methods:

  • Developed a collaborative filtering-based matrix completion model named Graph Attention Collaborative Filtering (GATCF).
  • Integrated graph attention mechanisms to capture peer interactions and dependencies.
  • Applied matrix completion techniques to recover missing data points in peer selection.

Main Results:

  • The GATCF model demonstrated effective recovery of missing values in the data matrix.
  • Experimental results on a large-scale dataset confirmed the model's superior performance.
  • Achieved higher recovery accuracy compared to existing methods in mitigating data sparsity.

Conclusions:

  • GATCF effectively mitigates data sparsity challenges in peer selection for BaaS.
  • The proposed model enhances the reliability of choosing trusted blockchain peers.
  • Graph attention and collaborative filtering integration offers a robust solution for decentralized systems.