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We have discussed why we form relationships, what attracts us to others, and different types of love. But what determines whether we are satisfied with and stay in a relationship? One theory that provides an explanation is social exchange theory. According to social exchange theory, we act as naïve economists in keeping a tally of the ratio of costs and benefits of forming and maintaining a relationship with others (Rusbult & Van Lange, 2003).
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Related Experiment Video

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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Surrogate explanations for role discovery on graphs.

Eoghan Cunningham1,2, Derek Greene1,2

  • 1School of Computer Science, University College Dublin, Dublin, Ireland.

Applied Network Science
|May 30, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for interpreting roles in large graphs using graphlets, enhancing explainable artificial intelligence for role discovery in complex networks.

Keywords:
Explainable artificial intelligenceNode embeddingRole discovery

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Area of Science:

  • Graph theory
  • Network analysis
  • Explainable artificial intelligence

Background:

  • Role discovery in graphs is crucial for understanding network structure.
  • Graph embedding methods are common but lack interpretability for large networks.
  • Explainable AI advancements offer new approaches for network analysis.

Purpose of the Study:

  • To propose a novel framework for interpretable role discovery in large graphs.
  • To leverage graphlets for explaining role assignments in complex networks.
  • To enhance the validation of identified roles in real-world networks.

Main Methods:

  • Developed a surrogate explanation framework for role discovery.
  • Utilized graphlets (small subgraph structures) for interpretation.
  • Applied the framework to both synthetic and real-world citation networks.

Main Results:

  • Demonstrated framework effectiveness on a synthetic graph.
  • Successfully identified significant citation patterns in a large multidisciplinary network.
  • Revealed interdisciplinary research structures through role interpretation.

Conclusions:

  • The proposed framework enhances the interpretability of role discovery in large graphs.
  • Graphlet-based explanations provide insights into network structures and research patterns.
  • This approach advances explainable AI applications in network science.