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Collaboration prediction in heterogeneous academic network with dynamic structure and topic

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Abstract

Academic collaborations improve research efficiency and spur scientific innovation. However, scholarly big data has hindered scholars from finding suitable collaborators. Although some studies have involved the prediction problem of academic collaborations, they neglect the rich dynamic information of the heterogeneous academic network. In this paper, we propose a prediction model for academic collaborations, which considers both the dynamic structure and content information. We first formally define the dynamic academic network and the collaboration prediction problem. Then, a scholar representation model is designed by capturing both the dynamic structure and content features, together with the macro-impact of overall academic trends. Finally, we build the prediction model based on the representation result of scholars. Extensive experiments for predicting new collaborations are conducted on the DBLP dataset. The experimental results on the accuracy, F1, and AUC metrics demonstrate that our method outperforms the baseline methods and can predict academic collaborations efficiently.

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Notes

  1. https://academic.microsoft.com.

  2. https://jcr.clarivate.com/JCRJournalHomeAction.action.

  3. https://academic.microsoft.com/conferences.

  4. https://dblp.uni-trier.de/xml/.

  5. https://pytorch.org/.

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Acknowledgements

The authors would like to thank the editor and the anonymous reviewers for their constructive comments. This work was supported by the National Nature Science Foundation of China [grant number 61671157] and the Major Project of Philosophy and Social Science Research, Ministry of Education of China [grant number 19JZD010].

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  1. Shanghai Key Laboratory of Data Science, School of Software, Fudan University, No. 220 Handan Road, Shanghai, 200433, China

    Weidong Zhao & Shi Pu

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Zhao, W., Pu, S. Collaboration prediction in heterogeneous academic network with dynamic structure and topic. Knowl Inf Syst 63, 2053–2074 (2021). https://doi.org/10.1007/s10115-021-01580-6

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