Proximity-aware research leadership recommendation in research collaboration via deep neural networks

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

18 Scopus Citations
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Original languageEnglish
Pages (from-to)70-89
Journal / PublicationJournal of the Association for Information Science and Technology
Issue number1
Online published28 Jun 2021
Publication statusPublished - Jan 2022


Collaborator recommendation is of great significance for facilitating research collaboration. Proximities have been demonstrated to be significant factors and determinants of research collaboration. Research leadership is associated with not only the capability to integrate resources to launch and sustain the research project but also the production and academic impact of the collaboration team. However, existing studies mainly focus on social or cognitive proximity, failing to integrate critical proximities comprehensively. Besides, existing studies focus on recommending relationships among all the coauthors, ignoring leadership in research collaboration. In this article, we propose a proximity-aware research leadership recommendation (PRLR) model to systematically integrate critical node attribute information (critical proximities) and network features to conduct research leadership recommendation by predicting the directed links in the research leadership network. PRLR integrates cognitive, geographical, and institutional proximity as node attribute information and constructs a leadership-aware coauthorship network to preserve the research leadership information. PRLR learns the node attribute information, the local network features, and the global network features with an autoencoder model, a joint probability constraint, and an attribute-aware skip-gram model, respectively. Extensive experiments and ablation studies have been conducted, demonstrating that PRLR significantly outperforms the state-of-the-art collaborator recommendation models in research leadership recommendation.