The impact of splitting granularity on multi-agent diffusion computing

Diffusion geometry offers a fresh perspective on multi-scale information analysis. However, there is still a lack of work on distributed approach to diffusion computing. In this paper, we propose a multi-agent diffusion approach where a massive data set is split into several subsets and each diffusion agent only needs to work with one subset in diffusion computation. We apply it to a large set of human decisionmaking experiences. The result indicates that the multi-agent diffusion approach is beneficial, and the system performance could be affected significantly by the splitting granularity (size of each splitting unit). This study encourages further theoretical investigations on the potential impacts of splitting granularity on the recoverability of the global diffusion map.