Yannis Karmim’s website

Here is my academic website, I present my PhD on GNN for Dynamic Graph and various other works.

In many situations, graphs represent data that are intrinsically dynamic (communications between people, financial exchanges, etc.). However, due to a lack of consensus on modeling, static graphs are often used, aggregating more or less important periods of time and thus destroying information. Graph Neural Networks have also been designed from static graphs (possibly series of snapshots). And it is only recently that models have emerged to take into account a continuous time and have proposed applications. Several questions are therefore open, in order to move towards better GNN models able to integrate the temporal dimension of data, to efficiently combine spatial and temporal diffusion of information. The transition to large scale, the explicability of the models, as well as a standardization of the evaluation are also issues for the coming years in the field.