Knowledge Network Simulator

Knowledge Network

Link strength 0.9

Repulsion 14

Growth rate 2.5 s

Topics

–

Connections

–

Avg. link length

–

Network energy

–

How it works

Every article on a blog is a node; a shared theme between two articles — VR in education linking to AI in research, quantum computing linking to quantum networks — is an edge. This simulator grows that graph live: a new topic node appears every few seconds and attaches to one or more existing nodes it shares a theme with, then a force-directed layout (a classic spring-electrical model) settles the whole structure so related topics drift close together and unrelated ones spread apart.

F_spring = -k · (|d| - L₀) · d̂     (per edge, Hooke's law)
F_repel  = c / |d|²  · d̂           (every node pair, Coulomb-like)
v += (F_spring + F_repel) · dt ;  pos += v · dt ;  v *= damping

Link strength (k) — how firmly a shared-theme edge pulls its two topics toward a natural resting distance; higher values snap the graph into tight clusters faster.
Repulsion — the electrostatic-style push every topic exerts on every other one, which is what keeps the whole graph from collapsing into a single point.
Growth rate — how often a new article/topic node is published into the network and wired to related neighbours.
Network energy — total remaining kinetic energy of all nodes; it falls toward zero as the layout relaxes into equilibrium after each new arrival.

Real-world relevance: this is the same force-directed graph-drawing technique (Fruchterman–Reingold / spring-electrical models) used by citation maps, recommendation engines and knowledge graphs to reveal which ideas cluster together purely from their connections, with no manual layout.

Science & research Technology & AI Education & society

⚠ Couldn't load the 3D engineThree.js failed to load from the CDN. Check your connection and reload.