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Updated: Jan 27, 2026

A Protocol for Computer-Based Protein Structure and Function Prediction
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Predicting Citation Count of Scientists as a Link Prediction Problem.

Ertan Butun, Mehmet Kaya

    IEEE Transactions on Cybernetics
    |March 16, 2019
    PubMed
    Summary

    This study introduces a novel supervised link prediction method to forecast scientists' future citation counts (PCCS). The approach leverages complex network topology, outperforming existing metrics in predicting new links and their weights.

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    Area of Science:

    • Bibliometrics
    • Network Science
    • Scientometrics

    Background:

    • Existing research primarily focuses on predicting paper citation counts (PCCP), with limited work on individual scientist impact.
    • Estimating future scientist influence is crucial for research planning and collaborations.

    Purpose of the Study:

    • To propose a novel supervised link prediction method for predicting scientist citation counts (PCCS).
    • To address the under-exploration of citation network topology in impact prediction.

    Main Methods:

    • Formulating PCCS as a link prediction problem in directed, weighted, and temporal citation networks.
    • Developing a supervised approach that predicts both links and their weights.
    • Testing the method on two real-world citation networks.

    Main Results:

    • The proposed method demonstrates promising performance in predicting links and their weights, a novel contribution to link prediction in complex networks.
    • Experiment 2 showed the proposed link prediction metric significantly outperformed five established baseline metrics.

    Conclusions:

    • The developed supervised link prediction method effectively estimates individual scientist impact.
    • This approach offers a more comprehensive way to analyze and predict scientific influence by utilizing network structures.