As graph embedding research evolves, contributing to this research requires understanding prevailing challenges that current state-of-the-art models may not fully address. Navigating this dynamic landscape, however, is challenging due to the abundance of literature and the complexity, especially for newcomers to the field. Although review articles aim to synthesize information to solve this challenge, they can be intricate and encompass numerous models, some of which may lack widespread recognition within the research community. Our investigation focuses on fundamental embedding techniques that have made significant contributions and serve as embedding strategies in this field. In addition to categorizing them, we reproduce these repositories to evaluate their performance in similar settings, analyze their limitations, and point to future research. This approach aims to provide a concise yet comprehensive overview, facilitating accessibility for researchers entering the domain.