As digitization and edge technologies become more mature and reliable, countless digital entities, connected devices, and microservices are interacting in purposeful ways, generating massive volumes of poly-structured digital data. Organizations are constantly searching for innovative ways to leverage this data to drive business transformation and disruption. Data science has become the go-to approach for simplifying knowledge discovery and sharing from these vast, multi-structured datasets.
With the support of query languages, databases, algorithms, platforms, and advanced analytics—including machine and deep learning—graphs are emerging as a powerful data structure for representing complex data and their intricate relationships.
Unlike traditional analytics, graph analytics leverages the interconnectedness of data points to identify clusters and relationships based on influence, association, interaction frequency, and probability. Cutting-edge graph analytics techniques are enabling the discovery of valuable connections among entities such as organizations, individuals, and transactions. This edited volume explores the many facets and significance of graph data science, with contributions from both academia and industry. It covers algorithms, analytics methods, platforms, and databases that can create business value by intelligently utilizing connected data.
This book is a valuable resource for researchers, scientists, engineers, lecturers, and advanced students in ICT, data analytics, data science, cloud/fog/edge computing, IoT, AI, machine and deep learning, and related fields. It will also interest analytics professionals and IT operations teams in industry.
