NETWORKS & CONTAGION

Six degrees of separation is not a figure of speech, it is a geometric consequence of a few shortcuts. Take a ring of near neighbours, add a handful of edges cutting across, and the average distance between any two nodes drops to a few steps: the small-world structure described by Watts and Strogatz. With the same node and edge counts, preferential attachment in the Barabási–Albert style instead breeds a few super-hubs, so once a hub is infected the red wave sweeps the graph far faster than in a random network of even degree.

▸ Building the graph · seeding nodes & edges…
▸ Loading the SIR model (susceptible · infected · recovered)
▸ Implanting patient zero
▸ Calibrating β (infect) & γ (recover)
▸ Letting the epidemic spread along the network…
▸ Ready — Online. ✅
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⌂ Complex systems

Simulation room Networks & contagion

Networks
Online
SIR · no leader
Epidemic state
🕸️ Mạng
current S · I · R ratio
Nodes—
% susceptible (S)—
% infected (I)—
% recovered (R)—
R₀ = β / γ—
Average degree—
The idea
No node "controls" the epidemic — each node only infects a few neighbours of its own. Whether the epidemic flares large or fizzles emerges from the network structure & the two numbers β, γ.
—
Node color = state (susceptible · infected · recovered) · big node = many connections · click a node to infect it
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Epidemic curve — S / I / R over time susceptibleinfectedrecovered
About Networks & Spreading

Six degrees of separation is not a figure of speech, it is a geometric consequence of a few shortcuts. Take a ring of near neighbours, add a handful of edges cutting across, and the average distance between any two nodes drops to a few steps: the small-world structure described by Watts and Strogatz. With the same node and edge counts, preferential attachment in the Barabási–Albert style instead breeds a few super-hubs, so once a hub is infected the red wave sweeps the graph far faster than in a random network of even degree.

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