Graph Data is a data architecture optimised for highly connected data. Graph Data works well with analytics methods based upon the mathematics of “Graph Theory”, initially developed by Euler. It is used for analysis of networks (in all forms – people, relationships, neural, ideas, costs, work, natural language etc.) and is the basis of the emerging field of Network Science. Graph Analysis is a best method for detecting and analysing patterns. Many modern AI approaches use graph structures “under the hood”.
Graph.Direct is the creation of a group of like-minded individuals seeking to simplify complexity, bring the best minds to solve world problems and realise the virtual and hyper realities they have dreamed of for years.
Simply a Hypergraph is a graph of graphs. In data engineering – turning that theoretical math into an executable reality – they are hyper extensible, extremely fast, extra-ordinarily scalable networks of networks. They are interesting because graphs can be used to analyse anything: knowledge, assets, people, decisions, money, value, processes – even time. With hypergraphs you can connect these graphs with other graphs, transforming your understanding in continuously new ways.
This is very powerful for insight analysis – uniquely able to find unknown unknowns.
It is extremely powerful as an underlying data platform for AI.
This model is proposed as a data structure to understand physics and our reality – a best path of multiple potential causalities – what are called multi-causal hypergraphs. You can see a snippet about Hypergraphs in physics from one of many great interviews with Lex Fridman on YouTube here: https://youtu.be/YGNaRFEa8PI