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Academic Journal of Engineering and Technology Science, 2021, 4(2); doi: 10.25236/AJETS.2021.040215.

Implementation of Graph Embedding Algorithms in Node Classification for Wiki


Tong Wu, Ying Li

Corresponding Author:
Tong Wu

New Media College, Communication University of Zhejiang, Hangzhou, China


As wiki data integration has become one of the most popular and freely available data sources on the Internet, more and more domestic and foreign researchers are studying and analyzing wiki data in-depth, However, when classifying nodes in wiki data sets, we encountered poor relevance of node classification, which led to the problem of reduced data quality, accuracy, and reliability. In response to existing problems, we propose to use graph embedding related algorithms to classify wiki data sets. To improve the accuracy of the classification task, we have implemented Deepwalk (Deep Walk Random Algorithm), Node2vec, and SDNE (Structural Deep Network Embedding) The algorithm is tested on the wiki data set. The final experimental results show that the Graph Embedding algorithm has a good effect on the node classification of graph structure data, and the SDNE algorithm is the best inaccurate.


Wiki dataset, Graph Embedding, Node Classification

Cite This Paper

Tong Wu, Ying Li. Implementation of Graph Embedding Algorithms in Node Classification for Wiki. Academic Journal of Engineering and Technology Science (2021) Vol. 4, Issue 2: 89-97. https://doi.org/10.25236/AJETS.2021.040215.


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