Graph analytics is critical in domains such as social network analysis, logistics, and cybersecurity. NetworkX is one of the most popular Python libraries in this space, widely appreciated for its ease of use and comprehensive collection of algorithms. However, as graph datasets increase in size and complexity, NetworkX’s CPU-based computations become a significant bottleneck, leading to slow processing times.

Data leaders are adapting to the profound shift brought about by GenAI. As organizations incorporate AI into their data strategies, Graph Retrieval-Augmented Generation is emerging as a transformative solution, bridging the gap between AI and Data. This post explores GraphRAG and how it integrates into your broader data strategy.

We can think of information existing in a continuous stream or in discrete chunks. Large Language Models (LLMs) fall under the category of continuous knowledge representation, while Knowledge Graphs belong to the discrete realm. Each approach has its merits, and understanding the implications of their differences is essential.

To build a WMG, the LLM processes a question and returns a graph of nodes using URLs as identifiers, these URLs link to ground truths stored in the organisation's Knowledge Graph. The WMG can also incorporate nodes representing conceptual understanding, establishing connections between the LLM's numerical vectors and the KG's ontological classes.