Abstract
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.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Academic Journal of Engineering and Technology Science
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
