• Dr.V.Sulochana
• S.Suriya
• S.Vasanth

Network embedding successfully maintains the network structure by assigning network nodes to low dimensional representations. A considerable amount of progress has recently been achieved in the direction of this new paradigm for network research. In this study, we concentrate on classifying, analyzing, and pointing out the future directions for network embedding techniques research. We begin by summarizing the purpose of network embedding. We talk about network embedding and how it relates to traditional graph embedding methods in a cognitive radio context. Following that, we give a thorough overview of a variety of network embedding techniques in a methodical way, including advanced information preserving network embedding techniques, network embedding techniques with side information, and approaches that preserve structure and properties. Additionally, many methods of network embedding assessment as well as certain practical online tools, such as network data sets and software, are explored. In our last section, we cover the foundation for utilizing these network embedding techniques to create a successful system and identify some possible future paths.

Network embedding ,Low-dimensional representations, Network structure, Graph embedding methods, Cognitive radio Advanced ,information preservation

"MACHINE LEARNING TECHNIQUES FOR 5G AND BEYOND USING PSO ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.a112-a117, April-2024, Available :http://www.jetir.org/papers/JETIR2404016.pdf

"MACHINE LEARNING TECHNIQUES FOR 5G AND BEYOND USING PSO ALGORITHM", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppa112-a117, April-2024, Available at : http://www.jetir.org/papers/JETIR2404016.pdf