This survey paper provides a comprehensive overview of network embedding methods, which aim to assign nodes in a network to low-dimensional representations while preserving the network structure. The paper categorizes and reviews various network embedding methods, including structure- and property-preserving methods, those incorporating side information, and advanced information-preserving methods. It discusses the motivations behind network embedding, the challenges of traditional network representations, and the benefits of network embedding over traditional methods. The paper also reviews classical graph embedding algorithms and their relationship with network embedding. Additionally, it presents evaluation approaches and useful online resources, such as datasets and software. Finally, the paper discusses the framework for building effective systems using network embedding methods and highlights potential future research directions.This survey paper provides a comprehensive overview of network embedding methods, which aim to assign nodes in a network to low-dimensional representations while preserving the network structure. The paper categorizes and reviews various network embedding methods, including structure- and property-preserving methods, those incorporating side information, and advanced information-preserving methods. It discusses the motivations behind network embedding, the challenges of traditional network representations, and the benefits of network embedding over traditional methods. The paper also reviews classical graph embedding algorithms and their relationship with network embedding. Additionally, it presents evaluation approaches and useful online resources, such as datasets and software. Finally, the paper discusses the framework for building effective systems using network embedding methods and highlights potential future research directions.