The first paper on which I worked during my PhD is about detecting changes in sequences of graphs using non-Euclidean geometry and adversarial autoencoders. As a real-world application of the method presented in the paper, we showed that we could detect epileptic seizures in the brain, by monitoring a stream of functional connectivity brain networks.
In general, the methodology presented in the paper can work for any data that:
- can be represented as graphs;
- has a temporal dimension;
- has a change that you want to identify somewhere along the stream of data;
- has i.i.d. samples.
There are a lot of temporal networks that can be found in the wild, but not many datasets respect all the requirements at the same time. What’s more, many public datasets have very little samples along the temporal axis.Read more