Knowledge Graphs & Alpha
Knowledge as compression, surprise as signal — and why graphs make second-order risk cheap to query
Abstract
Everyone in Web3 maintains an internal model of the industry — conferences, podcasts, an unhealthy amount of crypto Twitter — and it is never complete enough for the decisions at hand. In information-theoretic terms, alpha lives in surprise: observations that force the model to update carry the signal, while confirmations carry almost none. Knowledge graphs make exactly those rare, structural relationships — shared investors, hidden dependencies, second-order contagion paths — cheap to query, which is what turns a data pile into an insight machine.
This piece is in draft — the graph queries and worked examples are still being cleaned up. The core argument is already public: Valeriy Zamaraiev's Why Knowledge Graphs Generate Alpha lays out the shared ground — knowledge as a compressed world-model, surprise as the unit of signal, and graph structure as the cheapest path to second-order risk. Start there; the two reads will meet in the middle.