Y NOTE 2

There are many forms of graphs. In the node and edge sense this is a graph where there are entities, Nodes, which hold data. These nodes could be represented in circles and there are edges which are association of one node to another. These edges can be represent in lines from one node to another one. At a networking event the nodes could be all the attendees and the edge could be agreements and details of these made by attendee that met each other and hold information of what the action request they agreed on was. One can capture an active and rapid networking event in one representation. This makes a more in depth observation possible. A high level representation of everything that was agreed at the event with ability to deep dive into individual agreement and get to analyse more about the Node entities and Edge associations of them i.e this is the ability of Node and Edges Graph representation. Everything can be a Node or an Edge although knowing when something is a Node or an Edge is a skill in graph representation. An easy solution is to say nodes are things and edges are concepts. Node are more constant or to just more persistent. Edges are more dynamic, easier to setup and easier to dismantle. You can then put yourself in the picture. It become simple to setup when  everything you encounter is a Node and your association to it is a concept. Your iPhone stand is a Node and the concept you own it the Edge. Your business card is a Node and all the instances you gave it out are edges. All these edges lead to other nodes of the contacts the business card was given to. The edges where significant follow up were done means the edge then become more significant and can represented with a stronger line. Being able to understand and represent this means we can represent a significant volume of concepts and associations. It is then simpler to evaluate advantages gained and lost with ability to reassess how or why these were gained to help learn to increase the amount of them. When the concept and associations are isolated and this graph representation is done its simple to know associations and increase these. The ability of this graph representation does not stop there. When you change the Nodes to other things to for example metal objects on a train engine that function together. The Nodes are these metal objects and edges are the level of quality one metal objects needs to be not to damage another metal object it is connected to. The edges state the level of quality needed and influence the level of quality of the metal objects needed. This gives an approximation of all the metal objects level of quality needed, the areas where higher level of quality is needed I.e. Potential fail Areas, areas where high quality would mean longer lasting and more. The graph represents levels of quality needed. With slight changes to the graph representation it can represent other a lot more.

This graph representation allow us to visualise this in a concept that's easy to understand in a short time scale. 

 It means we can start to begin to have larger understanding of everything. It becomes much simpler largely because this is loosely how we represent our knowledge. In neurons and synaptic connections the knowledge is represented. In this graph represent we present the knowledge similarly and it's easy to comprehend. There are many graph representation that can help and many allow searching of this for one concept to know everything that is associated with the concept.