Telling ecological networks apart by their structure: A computational challenge

Ecologists have been compiling ecological networks for over a century, detailing the interactions between species in a variety of ecosystems. To this end, they have built networks for mutualistic (e.g., pollination, seed dispersal) as well as antagonistic (e.g., herbivory, parasitism) interactions. The type of interaction being represented is believed to be reflected in the structure of the network, which would differ substantially between mutualistic and antagonistic networks. Here, we put this notion to the test by attempting to determine the type of interaction represented in a network based solely on its structure. We find that, although it is easy to separate different kinds of nonecological networks, ecological networks display much structural variation, making it difficult to distinguish between mutualistic and antagonistic interactions. We therefore frame the problem as a challenge for the community of scientists interested in computational biology and machine learning. We discuss the features a good solution to this problem should possess and the obstacles that need to be overcome to achieve this goal. Author summary In the late 1960s, Mark Kac asked, Can one hear the shape of a drum? challenging readers to reconstruct the geometry of the drum from a provided list of overtones. Physicists and mathematicians found that, although in general, one cannot hear the shape of a drum, many of its properties, such as its area and perimeter, can be heard. Here, we ask whether the type of interaction being represented in a network can be seen when inspecting its shape-for example, whether we can distinguish networks reporting interactions between plants and their pollinators (mutually beneficial) from those representing interactions between plants and their herbivores (beneficial only to the herbivores). We show that many types of nonbiological networks can be easily separated based on structural properties, whereas determining the type of interaction represented by an ecological networks is harder. We therefore turn this problem into a challenge for the scientific community. We argue that solving this problem would greatly benefit the field, and we discuss what the consequences of a failure might be.