As the human world becomes more digital, our connections and interactions are recorded and shared. We go from knowing 150 people and analyzing a few stories a week to 2 billion people sharing hundreds of millions of stories constantly. But humans still need to understand what's going on underneath. In this entry, we want to highlight how massive, machine scale systems are visualized through mathematical methods to tell new stories. These charts -- giant sprawling data webs like airplane traffic patterns etched onto the globe -- are the future of literacy in the machine age.
In the first example, we borrow two images from Google. The Google Cloud team created a service which grabs the entire Ethereum blockchain, backs it up on Cloud, and makes it easier to analyze. The first image shows the Crypto Kitty universe, with color attached to owner of the contract (kitty whales!) and size of the bubble ranking the quality of the asset. We can certainly imagine this done on regular old financial assets. The second visualization is for transactions: points are wallets and lines are asset movement. You can immediately seen wallet clustering, which shows entities that have more frequent transactions between each other closer together. In this way, one can ferret out exchange wallets or bots. Hey there Bitfinex!
The second source is a ConsenSys write up on decentralized exchanges, and is truly a spectacular chart. Do yourself a favor and click to zoom in. The dataset comes from IDEX, EtherDelta, Bancor, 0x, OasisDex, Kyber Network, and Airswap Protocol -- today's decentralized exchanges. Each point is a trading pair, the width of the line is number of normalized trades, and the line colors signify the exchange used. You can immediately see the most popular trade contracts, as well as exchanges where trading hops through an intermediate token, rather than through ETH itself. We'd love to see this for traditional FX markets, or maybe all trading period!
The last chart is from Geoff Golberg, who mapped out all Twitter accounts engaged in the Ripple XRP community with the purpose of identifying bots. And yep, the 40,000 point cloud has multiple bot armies across the world used to manufacture opinions and drive social engagement. It takes a robust mathematical approach to visualize this information, and a detailed article written by a human to infer the relationships and their activities within the data network. This is a flavor of future skillsets required to thrive in a machine world.