The obtained sanitized data was used to assemble a network using graph-tool (2) in Python2.7, excluding all multi-step reactions. The network was assembled in working through the raw data set and adding each species which is not contained in the network yet as a node and labelling it with its Reaxys ID. Then an edge is added from each reactant in that reaction to each product in that reaction. Each edge is labelled with its publication year to screen the graph. This allowed to decrease the network's size by stepping back in time. This resulted in a network of 178,122 nodes and 401,258 edges prior to removal of parallel edges. This was used as base for the analysis of alternative stochastic block models. For the link prediction study itself it was decided to reduce the size of the network further for performance reasons. To achieve this a wiring scheme was adopted registering only the heaviest reactant and the heaviest product. Thus for each reaction only the heaviest product and heaviest reactant were added to the network (each again labelled with its Reaxys ID) and then an arrow was drawn connecting that reactant to that product which was again labelled with its publication year to allow screening. This yielded a much more condensed representation of chemistry now numbering 161,760 chemical species and 132,539 reaction edges.