Evolving Your Application of GDS Technology

Graph data science (GDS) is increasingly applied in business to achieve better decisions, improved predictions, and innovation through the use of graph technology. Knowledge graphs are a fundamental component of GDS, providing a structured way to integrate diverse information and add context to applications such as AI systems. Graph analytics involves using global queries and algorithms to analyze relationships and topology within networks, enabling businesses to answer specific questions and make informed decisions. The major phases of the GDS journey include knowledge graph implementation, graph analytics, graph feature engineering, and graph embedding, which are used to extract predictive elements from raw graph data for machine learning tasks. Graph networks represent a new approach to ML that leverages graphs to improve results with less data, making predictions more explainable, and enabling new types of learning.

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