Table 3 Convergence criteria during the simulations. The columns \(c_{edges}\) and \(c_{beliefs}\) provide the threshold values used as convergence criteria for the summed link weights and community belief homogeneity respectively. d is the absolute difference from the previous 1000 data points and p is the mean of the previous 1000 data points as a normalization factor. \(c_{count}\) is the number of consecutive convergence checks required to end the simulation.
From: The desire to avoid cognitive dissonance drives community formation in a social network model
N | M | TCA | \(c_{edges}\) | \(c_{beliefs}\) | \(c_{count}\) |
|---|---|---|---|---|---|
100 | 10 | \(f(x) = x\) | \(d/5000 < 0.5 \%\) | \(d/p < 5 \%\) | 200 |
100 | 10 | \(f(x) = x^2\) | \(d/5000 < 0.25 \%\) | \(d/p < 5 \%\) | 200 |
100 | 10 | \(f(x) = x^4\) | \(d/1000 < 0.1 \%\) | \(d/p < 5 \%\) | 200 |
250 | 10 | \(f(x) = x\) | \(d/10000 < 0.5 \%\) | \(d/p < 5 \%\) | 1000 |
250 | 10 | \(f(x) = x^2\) | \(d/10000 < 0.5 \%\) | \(d/p < 5 \%\) | 1000 |
250 | 10 | \(f(x) = x^4\) | \(d/1000 < 0.5 \%\) | \(d/p < 5 \%\) | 1000 |
