Graph algorithms are becoming increasingly important as graph data is growing in size and complexity, making it essential to develop efficient methods for analyzing and processing these graphs. Dr. Steven Skiena's research on graph embeddings, particularly with his algorithm DeepWalk, aims to translate graphs into numerical representations that can be used in machine learning models, enabling powerful applications such as question answering, similarity detection, and clustering. The concept of graph embeddings is analogous to word embeddings in natural language processing, where words are represented by their roles in sentences, and similarly, vertices are represented as points in space. DeepWalk's power lies in its ability to generate these representations using unsupervised methods, similar to Word2vec for text data. Graph algorithms have the potential to become even more powerful with advancements in distributed algorithms, such as hierarchical graph embedding, which enables the processing of larger graphs. The increasing importance and ubiquity of graph technology make it an exciting area of research, with Dr. Skiena's work contributing significantly to its development.