Shared vocabularies are not designed by any central authority; they emerge from the bottom up as communities of speakers repeatedly try to understand one another. We study this process with an agent-based Naming Game in which a population that begins with no common vocabulary self-organises a shared language through local pairwise interaction, and we ask how the social network governs the outcome under communication noise and under selection on communicative strategy. The study combines baseline experiments with an extended suite built specifically to meet the standards of replication and statistical inference: every reported quantity is averaged over many independent seeded runs with confidence intervals, and the central claim is tested with a formal interaction statistic. Convergence time scales with population size as a power law with exponent close to four-thirds, matching analytical results for the Naming Game on networks. Restricted networks sustain many coexisting regional dialects, which we quantify with a cluster-based metric and show to be metastable, decaying to a single language as the interaction budget grows. Our central methodological contribution concerns noise robustness. A naive comparison suggests that denser networks simply tolerate more noise, but by comparing topologies at matched mean degree and by sweeping degree directly we separate two effects: connectivity raises robustness with sharply diminishing returns, while at identical degree locally clustered topologies remain significantly less robust than well-mixed ones. The effect of topology therefore survives the removal of the degree confound. Finally, an evolutionary extension shows that selection for communicative success drives inventiveness towards a low level that a frequency-dependent reward for useful innovation raises only slightly, so conservatism is robust rather than an artefact of the fitness function.