Estimating network degree distributions under sampling: an inverse problem, with applications to monitoring social media networks

Social networks, whose edges represent frienwdships, often change over time due to increased/decreased social activity, formation of new friendships, loss of old friendships, etc. Brain networks, whose edges represent the transmission of an electrical charge between neurons, also change depending on brain function and activity. These are just two examples of networks with dynamic edge patterns. There are many more, with each admitting its own behaviors depending on the domain of application. In some scenarios, both vertex and edge sets change over time; in others, the edges associated to different vertices evolve on different time scales. In this final chapter I focus only on the most basic aspects of modeling dynamic networks whose vertex set stays fixed while its edges vary over time. I leave more nuanced considerations (e.g., dynamic vertex and edge sets, different time scales, etc.) to future developments …