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
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.
Our Leadership
Graph.Direct is led by a group of seasoned professionals, long recognised as leaders in the fields of data, information, knowledge and digital innovation.
Janet Brimson and Luke Housego have worked together for many years on the forefront of data innovation, context creation and strategic visioning. Both bring a level of proven innovation and thought leadership to the complexity of the hyperverse.
Janet Brimson
Chief Executive Officer
Luke Housego
Chief Data Officer
Ian Gordon
Commercial Director
Board & Advisory Team
Graph.Direct is overseen by a board comprising of company directors and senior advisors as well as an extended advisory team comprised of experts in the many facets of digital innovation across all industries.
FAQ
What are Hypergraphs?
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
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Contact
37 Crosby Road
Albion QLD 4010
E: hello@graph.direct