Scientific Collaboration Recommendation Based on Hypergraph

[Objective] To promote collaboration and academic community building among researchers, this paper proposes a hypergraph-based recommendation algorithm, SCRH. [Methods] Firstly, we constructed a scientific collaboration hyper-network based on hypergraph structure. Then, we created the hypergraph’s structural similarity index based on common neighbors and resource allocation. Next, we built the attribute similarity index of the hypergraph using the author topic model and deep autoencoder. Finally, the two measurement indices were linearly fused to achieve scientific collaboration recommendations. [Results] In the collaboration recommendation task, the AUC and MR index values of SCRH reached 0.88 and 2.35, which were 0.11 and 0.79 better than the optimal metrics of the comparison algorithms. [Limitations] SCRH only considers the author’s text attributes in the node attribute similarity measurement. It needs to fully utilize the author’s citation information, institution information, and publication levels. [Conclusions] SCRH considers the hypergraph’s structural and attribute features. It can effectively accomplish the research collaboration recommendation tasks in stem cells field. © 2023 Data Analysis and Knowledge Discovery. All rights reserved.