This survey provides a comprehensive analysis of graph embedding techniques, which have gained significant traction in recent years due to their ability to represent complex network structures in vector space. The authors introduce the embedding task and its challenges, such as scalability, dimensionality, and feature preservation. They categorize the approaches into three main categories: factorization methods, random walks, and deep learning, and provide examples of representative algorithms in each category. The performance of these methods is evaluated on common datasets and compared against each other. The survey also discusses potential applications and future research directions. Finally, the authors present GEM, an open-source Python library that implements all the discussed graph embedding methods, facilitating further research in this field. The library is available at <https://github.com/palash1992/GEM>.This survey provides a comprehensive analysis of graph embedding techniques, which have gained significant traction in recent years due to their ability to represent complex network structures in vector space. The authors introduce the embedding task and its challenges, such as scalability, dimensionality, and feature preservation. They categorize the approaches into three main categories: factorization methods, random walks, and deep learning, and provide examples of representative algorithms in each category. The performance of these methods is evaluated on common datasets and compared against each other. The survey also discusses potential applications and future research directions. Finally, the authors present GEM, an open-source Python library that implements all the discussed graph embedding methods, facilitating further research in this field. The library is available at <https://github.com/palash1992/GEM>.