Gradient-based Graph Structure Optimisation for Research Networks

How novel ideas are discovered and spread through a population is a well-studied phenomenon in the fields of computer science, computational social science, marketing/business research and higher education studies. In this paper, we study a particular version of this phenomenon, namely the spread of ideas among a network of researcher agents. We use a computational model in which each agent has a finite computational budget, that it can spend on a mixture of conducting its own research, and learning from the research of others in the network. By framing the problem as a continuous optimisation on the edge weights, we can applying gradient-based optimisation, similar to that from graph neural networks, to search for the graph configuration that maximises the amount of knowledge uncovered by the network. This contrasts with existing works that simply compare a fixed set of configurations. We find that (i) the optimisation succeeds in producing graphs that lead to a high total knowledge score, (ii) the optimum configuration has somewhat short shortest path lengths and a low clustering coefficient, (iii) the nodes end up broadly divided between researchers, who focus solely on their own research, and learners, who focus mostly on viewing research of others, and (iv) the learners consistently have higher knowledge scores, and this drives a rise in inequality across nodes over time.