Abstract To solve the problem of relationship prediction among literature network nodes, the similarity of nodes is regarded as the probability of relationship among nodes, and a network representation learning method is utilized to embed nodes into a low-dimensional space to calculate the similarity. Therefore, a meta-structure-based network representation learning model is proposed. According to the correlation between nodes based on different meta-structures, the network is mapped to a low-dimensional feature space by fusing their corresponding feature representations. The relationship prediction of literature network is realized by the distance measure in the low-dimensional feature space. Experiments indicate that the proposed algorithm obtains good relationship prediction results in literature network.
Fund:Supported by Natural Science Foundation of Fujian Province(No.2015J01420), Guiding Project of Fujian Province(No.2016Y0060), Health Education Joint Research Project of Fujian Province(No.WKJ2016-2-26)
Corresponding Authors:
YU Chunyan, Ph.D., associate professor. Her research interests include virtual environment and simulation technology, and intelligent algorithm.
About author:: WANG Xiu, master, lecturer. Her research interests include data mining. CHEN Lu, master student. Her research interests include heterogeneous information network.
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2020, Vol. 33 
