About the book
Graph theory being a rigorously investigated field of combinatorial mathematics is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further.
Hence, the book aims to gather both theoretical and applied aspects of graph theory within the broad context of machine learning. The book is to be enriched by algorithms and software codes (i.e. R, Python) for graph machine learning from network data manipulation, visualization to model deployment. After all, the book intends to introduce a unique collection of chapters to acquaint readers with the concepts and definitions from graph theory and machine learning, equipping them with basic tools to uncover challenging, yet joyful combinatorial problems.