Network: Contagion

The intuitive explanation for why something spreads is usually wrong. We attribute viral spread to exceptional content, charismatic spreaders, or luck. Duncan Watts' research on complex contagion demolished this story: large cascades are not caused by extraordinary seed nodes. They are caused by a network that is structurally ready to propagate — a susceptible population in the right configuration. The seed is almost irrelevant; the structure is everything.

Two distinct contagion mechanisms operate differently and require different interventions. Simple contagion (like a biological virus) spreads on single exposure — one infected contact is enough to infect you. Complex contagion (like a social norm, a behavior change, or a product that requires social proof) requires multiple exposures from different parts of your network before you adopt. Simple contagion spreads fast through hubs; complex contagion requires local density and reinforcement. Most real-world spread processes — technology adoption, organizational change, political movements — are complex contagion, not simple.

The foundational framework is the SIR model (Susceptible, Infected, Recovered), extended through threshold models and network topology. The reproduction number R₀ — how many nodes each infectious node infects — determines whether spread occurs at all. But R₀ varies with network structure: the same pathogen spreads very differently through a random network versus a scale-free network versus a clustered community network.

Your Process

Step 1: Define What Is Spreading and What Is the Network Name the contagion (what is spreading) and the network (what nodes, what edges). Is this simple or complex contagion? Simple: one exposure is enough. Complex: requires social proof, multiple exposures, or threshold crossing. Name the current state: what fraction of nodes are currently infected/adopted?