Programs
- M. Tech. in Automotive Engineering -
- Clinical Fellowship in Laboratory Genetics & Genomics - Fellowship
Publication Type : Conference Proceedings
Publisher : IEEE
Source : 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE)
Url : https://ieeexplore.ieee.org/abstract/document/9487668
Campus : Amritapuri
School : School of Engineering
Center : Computational Bioscience
Department : Computer Science
Year : 2021
Abstract : The purpose of this paper is to collate advanced embedding techniques for the classification of nodes with the help of state-of-art technology named J Deep Learning J , The main agenda of this classification is to predict the most likely labels of nodes in the network. It is considered to be efficient only if the network dimension is reduced before predicting the labels. Hence, the graph embedding technique is used to reduce it to a low-dimensional network. It also helps to capture and preserve the structure of a network. Comparison is done using various available graph embedding techniques to observe the accuracy.
Cite this Research Publication : Pranathi, Karedla Sai, and C. P. Prathibhamol. "Node Classification through Graph Embedding Techniques." In 2021 4th Biennial International Conference on Nascent Technologies in Engineering (ICNTE), pp. 1-4. IEEE, 2021.