LAUSR

Complex networks have been useful to link experimental data with mechanistic models, and have become widely used across many scientific disciplines. Recently, the increasing amount and complexity of data, particularly in biology, has prompted the development of multidimensional networks, where dimensions reflect the multiple qualitative properties of nodes, links or both. As a consequence, traditional quantities computed in single dimensional networks should be adapted to incorporate this new information. A particularly important problem is the detection of communities, namely sets of nodes sharing certain properties, which reduces the complexity of the networks, hence facilitating its interpretation. In this work, we propose an operative definition of ‘function’ for the nodes in multidimensional networks. We exploit this definition to show that it is possible to detect two types of communities: (a) modules, which are communities more densely connected within their members than with nodes belonging to other communities, and (b) guilds, which are sets of nodes connected with the same neighbours, even if they are not connected themselves. We provide two quantities to optimally detect both types of communities, whose relative values reflect their importance in the network. The flexibility of the method allowed us to analyse different ecological examples encompassing mutualistic, trophic and microbial networks. We showed that by considering both metrics we were able to obtain deeper ecological insights about how these different ecological communities were structured. The method mapped pools of species with properties that were known in advance, such as plants and pollinators. Other types of communities found, when contrasted with external data, turned out to be ecologically meaningful, allowing us to identify species with important functional roles or the influence of environmental variables. Furthermore, we found that the method was sensitive to community‐level topological properties like nestedness. In ecology there is often a need to identify groupings including trophic levels, guilds, functional groups or ecotypes. The method is therefore important in providing an objective means of distinguishing modules and guilds. The method we developed, functionInk (functional linkage), is computationally efficient at handling large multidimensional networks since it does not require optimization procedures or tests of robustness. The method is available at: https://github.com/apascualgarcia/functionInk.