New publication on network dynamics
Networks are now used to describe all sorts of systems – social worlds, protein interactions, food webs, and so on. For example, here you see a litter of marmot pups, where a network could be used to describe relationships between each individual animal. These networks are useful because they allow us to identify interesting structures and relationships, as well as test hypotheses about how groups function. For example, are individual marmots with larger body sizes more likely to have a higher number of dominant social relationships than smaller ones?
Networks like the one shown above (with individuals as dots, and relationships as lines) are useful but limited. Building a network implies that the system is stable – equivalent to assuming that relationships don’t change. This is problematic for many systems that do change over time. An individual marmot that is dominant as a juvenile may lose its dominance as an adult. And two marmots that appear to be able to interact in one network (that is, there is a path between their dots in the visual representation) may actually not be able to. Consider individuals A, B, and C. A interacts with B at 10AM; B interacts with C at 11AM. If we lump all these interactions together into one big network, we infer that A, B, and C are connected. But if the question is about the spread of some disease, then we should infer that A can spread disease to C but C cannot spread disease to A. We miss this with most networks.
These examples are only a few of the many issues that arise when networks become dynamic instead of static. I recently published a paper (with Tina Wey, Anna Dornhaus, Dick James, and Andy Sih) that surveys these issues and highlights multiple methods to resolve them and to better ask dynamic questions. You can read it in Methods in Ecology and Evolution here. A PDF is also available from my website. Please check it out!
It is one thing to point out issues, but another to provide practical tools. We also developed a free software package for R called timeordered which implements many of the algorithms and data analysis tools needed to ask questions about network dynamics. Above, you are seeing an object called a ‘time-ordered network’ that captures all the scientist’s information about the timing of interactions and the relationships between them. The network you see above can be thought of as a simplification of this more complex structure. Try the software out – it is useful for many kinds of data. We think this dynamic perspective will open up a whole new realm of questions to multiple areas of research.
Ecological networks, pollination networks (here shown using a Tetragonisca bee), disease networks… these are all dynamic systems. We hope this paper will bring us closer to dynamic understandings of them!