From my original post on Hacker News:
So the basic concept behind artificial neural networks is to have a graph with input and output nodes. You send logic through the graph, as a series of pipes. The logic within the graph (each node serves as I/O) processes the input values and provides output values based on the graph structure. You create the graph by training/conditioning the neural network – thus creating the logic.
What would come of applying this theory to social networks (and other occurrences of graph patterns throughout the internet)?
The reason that neural networking works is that the structures in the graph fundamentally represent patterns outside of the graph; effectively their logic mimics external mathematical patterns – they are a simplification of the information that the network digests as it is conditioned.
Extrapolating from this fact, shouldn’t the patterns (and hence the logic) within social networks hold meaning? What would be the equivalent of running “pulses” through the social graph?
There would be two ways to do this.
Internally – create a simple algorithm that uses the logic held in the social graph to process inputs and outputs. For example, you might send an array of integers through a subgraph of Faceboook and get some other integers out from different end nodes within the subgraph. (With internal graph processing, you’d need access to the database, so only the social network would have the ability to run analytics like this.)
Externally – literally send a piece of information (an email that get’s forwarded from one person (node) to the next, a tweet retweeted, or a hyperlink referenced) through the social network and follow it’s path through the network. It’s ending point would effectively be a function of it’s starting point, and it’s ending quality (for example, if the email was somehow changed by the end), would also be a function of it’s starting point.
What could be made of such analytics? Are people already doing this? (I assume they are… I can’t be the first one who thought of this.)