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Why Build a Stronger Network?
Intro to Graph Theory & Distribution
The concept that you are 6+ contacts away from anyone on earth has made it’s way into the general lexicon under the alias ‘6 Degrees of Separation’. If you aren’t familiar with the idea, the shortest possible description is above, but the slightly more elaborated explanation of the idea stems from something called Graph Theory and the study of networks.
Why should you care?
We live in a world more connected than ever. With a basic understanding of this concept, you can take stock in the idea and build bonds across the world that will change your life. Grandiose? Perhaps, but lucky for us, grand isn’t mutually exclusive from true.
What is Graph Theory?
Graph theory is the study of graphs, which are structures used to model relations between objects. A graph in this context is made up of nodes which are connected by edges. — Paraphrased, Wikipedia
In the example below, picture yourself as a ‘node’ and the relationships you share with others an ‘edge’.
Now let’s jump to an analogy based on a RAND Corp. Study that introduced us to the idea of ‘Decentralization’ in a graph.
RAND Corp completed this study sponsored by the United States Air Force under the context of exploring models to improve the security of communications networks.
I encourage you to continue however, viewing yourself as a node in the above 3 examples and consider the real world value that could come of building new edges not only between yourself and others, but also between other nodes in your network. The value of the latter may be intangible compared to the former, but returning to the conclusion of the initial case study most broadly - building a distributed network around yourself will create a more secure ecosystem to enable the flow of information.
“But Ben, I still don’t get why I should care” —
Folding the Graph
The most tangible reason to care about building edges between nodes in your network is because distributed graphs have a higher ROI when subjected to something called Graph Folding. In the image above, notice how all nodes across the three images are in the same location. Now picture yourself as the bottom left node.
In both the centralized and decentralized examples, that node has only one edge or relationship to the metaphor we’ve been carrying. Furthermore, if the graph was ‘folded’ across an axis, let’s say it’s to meet the upper right node, then the new resulting edges across the network may be disparate in the centralized example and serendipitous at best in the decentralized example.
In the distributed network however, not only does the specific node we’re looking at start with more connections, but in the event that the network folds, the newly resulting edges across the network will be far higher not to mention stronger because of the interconnectedness of the initial network graph.
“But Ben, I STILL, still don’t get why I should care” —
Hard work, integrity, industriousness, all these things no doubt matter. I can say first hand however that if they aren’t carried and promoted across a strong network then you are carrying a load twice as heavy through mud twice as deep- wherever it is you are headed.
Next week, I’m going to talk about some of the models that underlie games and how to apply them to the game we’re all playing now.
See you there.