[目的/意义]融合多元网络和网络表示学习方法学习并发现作者间的关联性，能够更好地进行合作者推
荐。[方法/过程]文章首先搜集情报学领域相关文献数据作为原始数据集，在经过数据清洗后，根据作者间的多元
关系构建各信息实体的多个科研信息网络，然后对高维网络利用Node2vec 网络表示学习方法学习各节点的信
息，从而得到各网络中节点的向量表示。其次，通过余弦相似度计算各网络中的作者相似度。最后融合作者间机
构合作偏好和作者学术水平相似度得到最终的推荐结果。[结果/结论]文章提出的融合模型考虑了多元网络和数
据稀疏性，在AUC 值上的表现优于单一维度，得到了更好的合作预测效果。实验结果表明，该合作者推荐模型在
情报学领域作者合作者推荐中具有可行性。

[Purpose/significance] Multiple networks is learned by network embedding to discover the relevance be?
tween authors and recommend collaborators better. [Method/process] First, collect relevant literature data in the field
of information science as the original data set. After data cleaning, multiple academic information networks of each in?
formation entity are constructed according to the multiple relationships between authors. Then use the Node 2vec to
learn the information of each node for the high-dimensional network, so as to obtain the vector representation of the
nodes in each network. Secondly, the author similarity in each network is calculated by cosine similarity. Finally, the fi?
nal recommendation result is obtained by integrating the institutional cooperation preference between authors and the
similarity of the authors' academic level. [Result/conclusion] The fusion model proposed in this paper considers multi ?
ple networks and data sparsity, and performs better than a single dimension in AUC value, and obtains a better predic?
tion effect for collaborators. The experimental results show that the collaborator recommendation model is feasible in
the author collaborator recommendation in the field of information science.