I currently have a Graph model whereby I am mapping connections of different types between entities and attributing a weight to these connections based upon my own personal experience. Also, I would like to understand the connections between these entities in relation to a particular outcome. Looking at this problem, I can't help but notice it's similarity to a typical Artificial Neural Network (ANN) and am wondering if/how I can bake some of the theory there into my model.
Let me explain further; I have a set of entities for which I have sets of connections. Each different type of connection has it's own individual weight, as shown in this diagram:
The weights in this diagram are estimated based on my own experience and can then be summed to look at the strength of a relationship between these entities. It is this strength of relationship that I want to understand in relation to a particular Output/Outcome.
I can't help but notice when looking at this particular application of Graph Theory that it bears a large resemblance to an Artificial Neural Network (ANN), as shown in this diagram;
The difference here being that whilst in the first diagram the nodes represent entities, in the second diagram the nodes represent input values. In the ANN, input values, randomized weights and the process of back-propagation are are used to help determine what the weights need to be whilst in the first diagram, these values are estimated. What I want to be able to do is to use randomized weights and the process of back-propagation to determine the weights in my Graph model that maps the connections between entities.
Does anyone have any experience of doing anything like this or could maybe at lest discuss the possibility of doing something like this?