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Prediction of Think Tank Experts' Collaborative Relationships Addressing Complex Issues

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  • Zhu, Jinxiao

Abstract

Purpose/Significance: Think tank research, as scientific inquiry, spans diverse disciplines. Factors like geography, time, and unfamiliarity hinder efficient expert team formation. Method/Process: This study analyzes/predicts collaboration potential in think tank expert networks to aid team assembly. Addressing the network's scale and rich attributes, we develop IeGNN (Information-exchange Graph Neural Network). It reduces computation by using ego-nets, aggregates neighbor attributes/information via weighted aggregation (using PersonalRank importance), and predicts links via cosine similarity. Result/Conclusion: IeGNN achieved an AUC of 0.9290 on our network, outperforming benchmarks. Case studies on three experts verified its feasibility.

Suggested Citation

  • Zhu, Jinxiao, 2025. "Prediction of Think Tank Experts' Collaborative Relationships Addressing Complex Issues," GBP Proceedings Series, Scientific Open Access Publishing, vol. 10, pages 56-62.
    • Handle: RePEc:axf:gbppsa:v:10:y:2025:i::p:56-62
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