Complements and competitors: Examining technological co-diffusion and relatedness on a collaborative coding platform

Diffusive and contagious processes spread in the context of one another in connected populations. Diffusions may be more likely to pass through portions of a network where compatible diffusions are already present. We examine this by incorporating the concept of "relatedness" from the economic complexity literature into a network co-diffusion model. Building on the "product space" concept used in this work, we consider technologies themselves as nodes in "product networks," where edges define relationships between products. Specifically, coding languages on GitHub, an online platform for collaborative coding, are considered. From rates of language co-occurrence in coding projects, we calculate rates of functional cohesion and functional equivalence for each pair of languages. From rates of how individuals adopt and abandon coding languages over time, we calculate measures of complementary diffusion and substitutive diffusion for each pair of languages relative to one another. Consistent with the principle of relatedness, network regression techniques (MR-QAP) reveal strong evidence that functional cohesion positively predicts complementary diffusion. We also find limited evidence that functional equivalence predicts substitutive (competitive) diffusion. Results support the broader finding that functional dependencies between diffusive processes will dictate how said processes spread relative to one another across a population of potential adopters.