Networks Decide for Us: Emergence of Adoption in Homophily-Imputed Social Topologies

The diffusion of collective behaviors is often modeled using synthetic network topologies, ignoring the empirical reality of multidimensional homophily. This paper asks: how do empirically calibrated topologies act as an autonomous causal force in diffusion outcomes? We impute plausible network structures using demographic distributions from attitudinal survey respondents (ATP 2014) and homophily interaction forces derived from the General Social Survey 2004 ego-module. Simulating a hybrid social contagion, we compare these empirical topologies against degree-preserving randomized baselines. We identify two structural mechanisms: First, empirical homophily yields a structural premium, reducing the odds of adoption failure by 57% through local echo chambers. Second, topology governs empirical criticality. Homophilic networks yield predictable, continuous phase transitions (Mean-Field universality, $\gamma \approx 1$), whereas randomized topologies collapse into discontinuous regime shifts. Ultimately, network structure acts as an active mechanism that decisively shapes the volume and dynamics of collective behavior.