基于网络表示学习的科研合作预测研究

Predicting Research Collaborations Based on Network Embedding

大数据环境下的科研合作预测亟需基于海量数据资源来自动学习和发现研究者间的关联性,提高预测效率和效果。首先基于海量数据构建合著网络,并以合著关系表示科研合作;接着基于深度学习的网络表示学习方法(network embedding)学习研究者在所处网络的语境信息,形成每个研究者的稠密、低维向量表示;最后通过向量相似度指标计算研究者间的语义相似度,实现科研合作预测和推荐。在图书情报领域的实验验证了该方法能够提高科研合作预测的准确率和效果,更好地进行关联推荐。该方法从数据科学视角丰富和扩展了基于复杂网络的情报分析方法。

In order to improve the efficiency and effect of predicting research collaborations in a large data environment, correlations among researchers should be learned and discovered automatically from massive datasets. Firstly, the co-authorship network is built from a massive dataset where research collaborations are indicated by co-authorship. Then, the researchers＇ context in the network is learned by network embedding based on the deep machine learning method, and each researcher＇s dense, low-dimensional vector is formatted. Finally, the semantic similarities among authors are calculated through the vector similarity indices for research collaboration prediction. Experiments in the field of Library and Information Science verify that the method can improve the accuracy and efficiency of research collaboration prediction. This method enriches and expands the information analysis methods based on complex networks from the perspective of data science.