基于超图的科研合作推荐研究

Scientific Collaboration Recommendation Based on Hypergraph

【目的】为促进科研人员间的合作交流与学术团体的构建,提出基于超图的推荐算法SCRH,用于干细胞领域的科研合作推荐研究。【方法】构建基于超图结构的科研合作超网络,然后基于共同邻居和资源分配构建超图的结构相似性指标,利用作者主题模型和深度自编码器构建超图的属性相似指标,最后将两种度量指标线性融合以实现科研合作推荐。【结果】SCRH在合作推荐任务上AUC和MR指标值为0.88和2.35,相较于对比算法最优指标度量分别提升0.11和0.79。【局限】SCRH在节点属性相似性度量中仅考虑作者的文本属性,没有充分利用作者的引用信息、机构信息和发文等级等属性信息。【结论】SCRH同时考虑了超图的结构特征与属性特征,能够有效完成干细胞领域的科研合作推荐任务。

[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.