Graph embeddings have been widely used for many graph analysis tasks. Mainstream factorization-based and graph-sampling-based embedding learning schemes both involve many hyperparameters and design choices. However, existing techniques often adopt some heuristics for these hyperparameters and design choices with little investigation into their impact, making it unclear what is the exact performance gains of these techniques on graph analysis tasks. Against this background, this paper presents a systematic study on the impact of an extensive list of hyperparameters for both factorization-based and graph-sampling-based graph embedding techniques for homogeneous graphs. We design generalized factorization-based and graph-sampling-based techniques involving these hyperparameters, and conduct a comprehensive set of experiments with over 3,000 embedding models trained and evaluated per dataset. We reveal that much of the performance gains are indeed due to optimal hyperparameter settings/design choices rather than the sophistication of embedding models; appropriate hyperparameter settings for typical embedding techniques can outperform a sizeable collection of 18 state-of-the-art graph embedding techniques by 0.30-35.41% across different tasks. Moreover, we find that there is no one-size-fits-all hyperparameter setting across tasks, but we can indeed provide a list of task-specific practical recommendations for these hyperparameter settings/design choices, which we believe can serve as important guidelines for future research on embedding based graph analyses.